Overview

Dataset statistics

Number of variables57
Number of observations709
Missing cells6535
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory315.9 KiB
Average record size in memory456.2 B

Variable types

Categorical49
Numeric8

Warnings

commune has constant value "Saint Germain en Laye" Constant
insecte_collet has constant value "Non" Constant
insecte_tronc has constant value "Non" Constant
insecte_houppier has constant value "Non" Constant
type_delai_1 has constant value "b" Constant
delai_preconisation_2 has constant value "a3" Constant
ID_ARBRE has a high cardinality: 709 distinct values High cardinality
reference_photo has a high cardinality: 231 distinct values High cardinality
diametre is highly correlated with circonference (en cm)High correlation
circonference (en cm) is highly correlated with diametreHigh correlation
cote_voirie has 164 (23.1%) missing values Missing
espece_arbre has 8 (1.1%) missing values Missing
insecte_collet has 10 (1.4%) missing values Missing
plaie_collet has 10 (1.4%) missing values Missing
observation_collet has 571 (80.5%) missing values Missing
champignon_tronc has 10 (1.4%) missing values Missing
insecte_tronc has 10 (1.4%) missing values Missing
fissure_tronc has 10 (1.4%) missing values Missing
rejet_tronc has 10 (1.4%) missing values Missing
plaie_tronc has 10 (1.4%) missing values Missing
observation_tronc has 633 (89.3%) missing values Missing
champignon_houppier has 10 (1.4%) missing values Missing
insecte_houppier has 10 (1.4%) missing values Missing
fissure_houppier has 10 (1.4%) missing values Missing
ecorce_incluse_houppier has 10 (1.4%) missing values Missing
bois_mort_houppier has 10 (1.4%) missing values Missing
plaie_houppier has 10 (1.4%) missing values Missing
observation_houppier has 572 (80.7%) missing values Missing
contrainte has 10 (1.4%) missing values Missing
classification_diagnostic has 10 (1.4%) missing values Missing
date_diagnostic has 10 (1.4%) missing values Missing
prescription_1 has 230 (32.4%) missing values Missing
prescription_2 has 542 (76.4%) missing values Missing
prescription_3 has 694 (97.9%) missing values Missing
observation_travaux has 619 (87.3%) missing values Missing
type_delai_1 has 701 (98.9%) missing values Missing
type_delai_2 has 230 (32.4%) missing values Missing
delai_preconisation_2 has 676 (95.3%) missing values Missing
delai_saison_programmation_2 has 256 (36.1%) missing values Missing
reference_photo has 478 (67.4%) missing values Missing
date_plantation is highly skewed (γ1 = -24.94358628) Skewed
ID_ARBRE is uniformly distributed Uniform
reference_photo is uniformly distributed Uniform
ID_ARBRE has unique values Unique
surf_permeable has 27 (3.8%) zeros Zeros

Reproduction

Analysis started2021-02-24 12:18:21.196792
Analysis finished2021-02-24 12:18:36.877394
Duration15.68 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

ID_ARBRE
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct709
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
78551-Arbres-245
 
1
78551-Arbres-044
 
1
78551-Arbres-117
 
1
78551-Arbres-125
 
1
78551-Arbres-258
 
1
Other values (704)
704 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters11344
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique709 ?
Unique (%)100.0%

Sample

1st row78551-Arbres-001
2nd row78551-Arbres-002
3rd row78551-Arbres-003
4th row78551-Arbres-004
5th row78551-Arbres-005
ValueCountFrequency (%)
78551-Arbres-2451
 
0.1%
78551-Arbres-0441
 
0.1%
78551-Arbres-1171
 
0.1%
78551-Arbres-1251
 
0.1%
78551-Arbres-2581
 
0.1%
78551-Arbres-4451
 
0.1%
78551-Arbres-4531
 
0.1%
78551-Arbres-2741
 
0.1%
78551-Arbres-6161
 
0.1%
78551-Arbres-3101
 
0.1%
Other values (699)699
98.6%
2021-02-24T13:18:37.114070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78551-arbres-4521
 
0.1%
78551-arbres-3941
 
0.1%
78551-arbres-2931
 
0.1%
78551-arbres-5131
 
0.1%
78551-arbres-6001
 
0.1%
78551-arbres-0851
 
0.1%
78551-arbres-0361
 
0.1%
78551-arbres-5541
 
0.1%
78551-arbres-0181
 
0.1%
78551-arbres-3471
 
0.1%
Other values (699)699
98.6%

Most occurring characters

ValueCountFrequency (%)
51659
14.6%
-1418
12.5%
r1418
12.5%
1950
8.4%
7860
7.6%
8850
7.5%
A709
6.2%
b709
6.2%
e709
6.2%
s709
6.2%
Other values (6)1353
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5672
50.0%
Lowercase Letter3545
31.2%
Dash Punctuation1418
 
12.5%
Uppercase Letter709
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
51659
29.2%
1950
16.7%
7860
15.2%
8850
15.0%
0248
 
4.4%
2241
 
4.2%
3241
 
4.2%
4241
 
4.2%
6241
 
4.2%
9141
 
2.5%
ValueCountFrequency (%)
r1418
40.0%
b709
20.0%
e709
20.0%
s709
20.0%
ValueCountFrequency (%)
-1418
100.0%
ValueCountFrequency (%)
A709
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7090
62.5%
Latin4254
37.5%

Most frequent character per script

ValueCountFrequency (%)
51659
23.4%
-1418
20.0%
1950
13.4%
7860
12.1%
8850
12.0%
0248
 
3.5%
2241
 
3.4%
3241
 
3.4%
4241
 
3.4%
6241
 
3.4%
ValueCountFrequency (%)
r1418
33.3%
A709
16.7%
b709
16.7%
e709
16.7%
s709
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII11344
100.0%

Most frequent character per block

ValueCountFrequency (%)
51659
14.6%
-1418
12.5%
r1418
12.5%
1950
8.4%
7860
7.6%
8850
7.5%
A709
6.2%
b709
6.2%
e709
6.2%
s709
6.2%
Other values (6)1353
11.9%

commune
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Saint Germain en Laye
709 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters14889
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaint Germain en Laye
2nd rowSaint Germain en Laye
3rd rowSaint Germain en Laye
4th rowSaint Germain en Laye
5th rowSaint Germain en Laye
ValueCountFrequency (%)
Saint Germain en Laye709
100.0%
2021-02-24T13:18:37.258907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:37.307530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
germain709
25.0%
en709
25.0%
saint709
25.0%
laye709
25.0%

Most occurring characters

ValueCountFrequency (%)
a2127
14.3%
n2127
14.3%
2127
14.3%
e2127
14.3%
i1418
9.5%
S709
 
4.8%
t709
 
4.8%
G709
 
4.8%
r709
 
4.8%
m709
 
4.8%
Other values (2)1418
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10635
71.4%
Uppercase Letter2127
 
14.3%
Space Separator2127
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
a2127
20.0%
n2127
20.0%
e2127
20.0%
i1418
13.3%
t709
 
6.7%
r709
 
6.7%
m709
 
6.7%
y709
 
6.7%
ValueCountFrequency (%)
S709
33.3%
G709
33.3%
L709
33.3%
ValueCountFrequency (%)
2127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12762
85.7%
Common2127
 
14.3%

Most frequent character per script

ValueCountFrequency (%)
a2127
16.7%
n2127
16.7%
e2127
16.7%
i1418
11.1%
S709
 
5.6%
t709
 
5.6%
G709
 
5.6%
r709
 
5.6%
m709
 
5.6%
L709
 
5.6%
ValueCountFrequency (%)
2127
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14889
100.0%

Most frequent character per block

ValueCountFrequency (%)
a2127
14.3%
n2127
14.3%
2127
14.3%
e2127
14.3%
i1418
9.5%
S709
 
4.8%
t709
 
4.8%
G709
 
4.8%
r709
 
4.8%
m709
 
4.8%
Other values (2)1418
9.5%

quartier
Categorical

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Quartier 2 - Alsace - Pereire
296 
Quartier 4 - Rotondes - St Léger
155 
Quartier 1 - Cœur de Ville et Quatier forestier
121 
Quartier 3 - Lycée International
97 
Quartier 6 - Hôpital
 
28

Length

Max length47
Median length32
Mean length32.83356841
Min length20

Characters and Unicode

Total characters23279
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQuartier 2 - Alsace - Pereire
2nd rowQuartier 2 - Alsace - Pereire
3rd rowQuartier 2 - Alsace - Pereire
4th rowQuartier 2 - Alsace - Pereire
5th rowQuartier 2 - Alsace - Pereire
ValueCountFrequency (%)
Quartier 2 - Alsace - Pereire296
41.7%
Quartier 4 - Rotondes - St Léger155
21.9%
Quartier 1 - Cœur de Ville et Quatier forestier121
17.1%
Quartier 3 - Lycée International97
 
13.7%
Quartier 6 - Hôpital28
 
3.9%
Quartier 7 - Debussy - Schnapper12
 
1.7%
2021-02-24T13:18:37.443619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:37.497201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1172
25.4%
quartier709
15.3%
2296
 
6.4%
alsace296
 
6.4%
pereire296
 
6.4%
léger155
 
3.4%
st155
 
3.4%
rotondes155
 
3.4%
4155
 
3.4%
et121
 
2.6%
Other values (14)1109
24.0%

Most occurring characters

ValueCountFrequency (%)
3910
16.8%
e3147
13.5%
r2758
11.8%
t1604
 
6.9%
i1493
 
6.4%
a1360
 
5.8%
-1172
 
5.0%
u963
 
4.1%
Q830
 
3.6%
l663
 
2.8%
Other values (30)5379
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15113
64.9%
Space Separator3910
 
16.8%
Uppercase Letter2375
 
10.2%
Dash Punctuation1172
 
5.0%
Decimal Number709
 
3.0%

Most frequent character per category

ValueCountFrequency (%)
e3147
20.8%
r2758
18.2%
t1604
10.6%
i1493
9.9%
a1360
9.0%
u963
 
6.4%
l663
 
4.4%
s596
 
3.9%
o528
 
3.5%
n458
 
3.0%
Other values (11)1543
10.2%
ValueCountFrequency (%)
Q830
34.9%
A296
 
12.5%
P296
 
12.5%
L252
 
10.6%
S167
 
7.0%
R155
 
6.5%
C121
 
5.1%
V121
 
5.1%
I97
 
4.1%
H28
 
1.2%
ValueCountFrequency (%)
2296
41.7%
4155
21.9%
1121
17.1%
397
 
13.7%
628
 
3.9%
712
 
1.7%
ValueCountFrequency (%)
3910
100.0%
ValueCountFrequency (%)
-1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17488
75.1%
Common5791
 
24.9%

Most frequent character per script

ValueCountFrequency (%)
e3147
18.0%
r2758
15.8%
t1604
9.2%
i1493
8.5%
a1360
 
7.8%
u963
 
5.5%
Q830
 
4.7%
l663
 
3.8%
s596
 
3.4%
o528
 
3.0%
Other values (22)3546
20.3%
ValueCountFrequency (%)
3910
67.5%
-1172
 
20.2%
2296
 
5.1%
4155
 
2.7%
1121
 
2.1%
397
 
1.7%
628
 
0.5%
712
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII22878
98.3%
None401
 
1.7%

Most frequent character per block

ValueCountFrequency (%)
3910
17.1%
e3147
13.8%
r2758
12.1%
t1604
 
7.0%
i1493
 
6.5%
a1360
 
5.9%
-1172
 
5.1%
u963
 
4.2%
Q830
 
3.6%
l663
 
2.9%
Other values (27)4978
21.8%
ValueCountFrequency (%)
é252
62.8%
œ121
30.2%
ô28
 
7.0%

site
Categorical

Distinct30
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
RN13
120 
Avenue du Maréchal Foch
111 
Rue Saint-Leger
74 
Avenue Gambetta
73 
Avenue Saint-Fiacre
55 
Other values (25)
276 

Length

Max length31
Median length15
Mean length15.03244006
Min length4

Characters and Unicode

Total characters10658
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.7%

Sample

1st rowCarrefour RN13
2nd rowCarrefour RN13
3rd rowCarrefour RN13
4th rowCarrefour RN13
5th rowCarrefour RN13
ValueCountFrequency (%)
RN13120
16.9%
Avenue du Maréchal Foch111
15.7%
Rue Saint-Leger74
10.4%
Avenue Gambetta73
10.3%
Avenue Saint-Fiacre55
7.8%
Rue Pereire55
7.8%
Place Christiane Frahier26
 
3.7%
Rue Président Roosevelt26
 
3.7%
Rue de Gramont24
 
3.4%
Rue Turgot21
 
3.0%
Other values (20)124
17.5%
2021-02-24T13:18:37.681893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rue264
15.6%
avenue242
14.3%
rn13129
 
7.6%
du121
 
7.1%
foch111
 
6.5%
maréchal111
 
6.5%
saint-leger74
 
4.4%
gambetta73
 
4.3%
saint-fiacre55
 
3.2%
pereire55
 
3.2%
Other values (43)460
27.1%

Most occurring characters

ValueCountFrequency (%)
e1563
14.7%
986
 
9.3%
a797
 
7.5%
u728
 
6.8%
r640
 
6.0%
n508
 
4.8%
t474
 
4.4%
i428
 
4.0%
R419
 
3.9%
c357
 
3.3%
Other values (36)3758
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7527
70.6%
Uppercase Letter1746
 
16.4%
Space Separator986
 
9.3%
Decimal Number258
 
2.4%
Dash Punctuation129
 
1.2%
Other Punctuation12
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e1563
20.8%
a797
10.6%
u728
9.7%
r640
8.5%
n508
 
6.7%
t474
 
6.3%
i428
 
5.7%
c357
 
4.7%
o321
 
4.3%
h293
 
3.9%
Other values (14)1418
18.8%
ValueCountFrequency (%)
R419
24.0%
A257
14.7%
F194
11.1%
S146
 
8.4%
N141
 
8.1%
P128
 
7.3%
M112
 
6.4%
G105
 
6.0%
L98
 
5.6%
C49
 
2.8%
Other values (7)97
 
5.6%
ValueCountFrequency (%)
1129
50.0%
3129
50.0%
ValueCountFrequency (%)
986
100.0%
ValueCountFrequency (%)
-129
100.0%
ValueCountFrequency (%)
'12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9273
87.0%
Common1385
 
13.0%

Most frequent character per script

ValueCountFrequency (%)
e1563
16.9%
a797
 
8.6%
u728
 
7.9%
r640
 
6.9%
n508
 
5.5%
t474
 
5.1%
i428
 
4.6%
R419
 
4.5%
c357
 
3.8%
o321
 
3.5%
Other values (31)3038
32.8%
ValueCountFrequency (%)
986
71.2%
1129
 
9.3%
3129
 
9.3%
-129
 
9.3%
'12
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10476
98.3%
None182
 
1.7%

Most frequent character per block

ValueCountFrequency (%)
e1563
14.9%
986
 
9.4%
a797
 
7.6%
u728
 
6.9%
r640
 
6.1%
n508
 
4.8%
t474
 
4.5%
i428
 
4.1%
R419
 
4.0%
c357
 
3.4%
Other values (34)3576
34.1%
ValueCountFrequency (%)
é170
93.4%
è12
 
6.6%

cote_voirie
Categorical

MISSING

Distinct2
Distinct (%)0.4%
Missing164
Missing (%)23.1%
Memory size5.7 KiB
Impair
308 
Pair
237 

Length

Max length6
Median length6
Mean length5.130275229
Min length4

Characters and Unicode

Total characters2796
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPair
2nd rowPair
3rd rowPair
4th rowPair
5th rowPair
ValueCountFrequency (%)
Impair308
43.4%
Pair237
33.4%
(Missing)164
23.1%
2021-02-24T13:18:37.849855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:37.905922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
impair308
56.5%
pair237
43.5%

Most occurring characters

ValueCountFrequency (%)
a545
19.5%
i545
19.5%
r545
19.5%
I308
11.0%
m308
11.0%
p308
11.0%
P237
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2251
80.5%
Uppercase Letter545
 
19.5%

Most frequent character per category

ValueCountFrequency (%)
a545
24.2%
i545
24.2%
r545
24.2%
m308
13.7%
p308
13.7%
ValueCountFrequency (%)
I308
56.5%
P237
43.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2796
100.0%

Most frequent character per script

ValueCountFrequency (%)
a545
19.5%
i545
19.5%
r545
19.5%
I308
11.0%
m308
11.0%
p308
11.0%
P237
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2796
100.0%

Most frequent character per block

ValueCountFrequency (%)
a545
19.5%
i545
19.5%
r545
19.5%
I308
11.0%
m308
11.0%
p308
11.0%
P237
8.5%

matricule_arbre
Real number (ℝ≥0)

Distinct74
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.84344147
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Memory size5.7 KiB
2021-02-24T13:18:37.968360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median17
Q333
95-th percentile59
Maximum74
Range73
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.78717647
Coefficient of variation (CV)0.8143028424
Kurtosis0.1056596719
Mean21.84344147
Median Absolute Deviation (MAD)12
Skewness0.9231753345
Sum15487
Variance316.3836469
MonotocityNot monotonic
2021-02-24T13:18:38.066463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133
 
4.7%
229
 
4.1%
329
 
4.1%
427
 
3.8%
524
 
3.4%
624
 
3.4%
721
 
3.0%
1120
 
2.8%
820
 
2.8%
1019
 
2.7%
Other values (64)463
65.3%
ValueCountFrequency (%)
133
4.7%
229
4.1%
329
4.1%
427
3.8%
524
3.4%
ValueCountFrequency (%)
742
0.3%
732
0.3%
722
0.3%
712
0.3%
702
0.3%

genre_arbre
Categorical

Distinct26
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Tilia
348 
Fraxinus
89 
Acer
53 
Corylus
49 
Ostrya
46 
Other values (21)
124 

Length

Max length26
Median length5
Mean length6.104372355
Min length4

Characters and Unicode

Total characters4328
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)1.1%

Sample

1st rowBetula
2nd rowBetula
3rd rowBetula
4th rowCarpinus
5th rowCarpinus
ValueCountFrequency (%)
Tilia348
49.1%
Fraxinus89
 
12.6%
Acer53
 
7.5%
Corylus49
 
6.9%
Ostrya46
 
6.5%
Carpinus38
 
5.4%
Prunus14
 
2.0%
Zelkova9
 
1.3%
PhotiniaxFraseri'RedRobin'8
 
1.1%
Betula8
 
1.1%
Other values (16)47
 
6.6%
2021-02-24T13:18:38.265384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tilia348
49.1%
fraxinus89
 
12.6%
acer53
 
7.5%
corylus49
 
6.9%
ostrya46
 
6.5%
carpinus38
 
5.4%
prunus14
 
2.0%
zelkova9
 
1.3%
betula8
 
1.1%
photiniaxfraseri'redrobin8
 
1.1%
Other values (16)47
 
6.6%

Most occurring characters

ValueCountFrequency (%)
i881
20.4%
a598
13.8%
l444
10.3%
T348
 
8.0%
r320
 
7.4%
s274
 
6.3%
u244
 
5.6%
n184
 
4.3%
e105
 
2.4%
C100
 
2.3%
Other values (28)830
19.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3579
82.7%
Uppercase Letter733
 
16.9%
Other Punctuation16
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
i881
24.6%
a598
16.7%
l444
12.4%
r320
 
8.9%
s274
 
7.7%
u244
 
6.8%
n184
 
5.1%
e105
 
2.9%
x97
 
2.7%
y96
 
2.7%
Other values (12)336
 
9.4%
ValueCountFrequency (%)
T348
47.5%
C100
 
13.6%
F100
 
13.6%
A65
 
8.9%
O46
 
6.3%
P29
 
4.0%
R17
 
2.3%
Z9
 
1.2%
B8
 
1.1%
M6
 
0.8%
Other values (5)5
 
0.7%
ValueCountFrequency (%)
'16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4312
99.6%
Common16
 
0.4%

Most frequent character per script

ValueCountFrequency (%)
i881
20.4%
a598
13.9%
l444
10.3%
T348
 
8.1%
r320
 
7.4%
s274
 
6.4%
u244
 
5.7%
n184
 
4.3%
e105
 
2.4%
C100
 
2.3%
Other values (27)814
18.9%
ValueCountFrequency (%)
'16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4328
100.0%

Most frequent character per block

ValueCountFrequency (%)
i881
20.4%
a598
13.8%
l444
10.3%
T348
 
8.0%
r320
 
7.4%
s274
 
6.3%
u244
 
5.6%
n184
 
4.3%
e105
 
2.4%
C100
 
2.3%
Other values (28)830
19.2%

espece_arbre
Categorical

MISSING

Distinct37
Distinct (%)5.3%
Missing8
Missing (%)1.1%
Memory size5.7 KiB
Europaea - - Pallida
319 
Angustifolia - L. - Raywood
65 
Colurna - L. -
49 
Carpinifolia - Scop. -
46 
Betulus - L. -
36 
Other values (32)
186 

Length

Max length40
Median length22
Mean length22.86019971
Min length5

Characters and Unicode

Total characters16025
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)1.4%

Sample

1st row Alba
2nd row Alba
3rd row Alba
4th row Betulus - L. - Fastigiata
5th row Betulus - L. - Fastigiata
ValueCountFrequency (%)
Europaea - - Pallida319
45.0%
Angustifolia - L. - Raywood65
 
9.2%
Colurna - L. - 49
 
6.9%
Carpinifolia - Scop. - 46
 
6.5%
Betulus - L. - 36
 
5.1%
Platanoides - - Columnare34
 
4.8%
Americana - - Autumn Applause16
 
2.3%
sp. - - 15
 
2.1%
Platyphyllos - - Flamme du Vercor14
 
2.0%
campestris - - Royal Ruby12
 
1.7%
Other values (27)95
 
13.4%
2021-02-24T13:18:38.455322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1389
47.5%
pallida319
 
10.9%
europaea319
 
10.9%
l174
 
6.0%
raywood70
 
2.4%
angustifolia70
 
2.4%
carpinifolia55
 
1.9%
colurna49
 
1.7%
scop46
 
1.6%
betulus38
 
1.3%
Other values (55)394
 
13.5%

Most occurring characters

ValueCountFrequency (%)
3796
23.7%
a1933
12.1%
-1389
 
8.7%
l1072
 
6.7%
o849
 
5.3%
i783
 
4.9%
u679
 
4.2%
r575
 
3.6%
e563
 
3.5%
p522
 
3.3%
Other values (36)3864
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9076
56.6%
Space Separator3796
23.7%
Uppercase Letter1477
 
9.2%
Dash Punctuation1389
 
8.7%
Other Punctuation287
 
1.8%

Most frequent character per category

ValueCountFrequency (%)
a1933
21.3%
l1072
11.8%
o849
9.4%
i783
8.6%
u679
 
7.5%
r575
 
6.3%
e563
 
6.2%
p522
 
5.8%
d466
 
5.1%
n341
 
3.8%
Other values (14)1293
14.2%
ValueCountFrequency (%)
P383
25.9%
E330
22.3%
L176
11.9%
C148
 
10.0%
A127
 
8.6%
R94
 
6.4%
S74
 
5.0%
B53
 
3.6%
K18
 
1.2%
F16
 
1.1%
Other values (8)58
 
3.9%
ValueCountFrequency (%)
.277
96.5%
'10
 
3.5%
ValueCountFrequency (%)
3796
100.0%
ValueCountFrequency (%)
-1389
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10553
65.9%
Common5472
34.1%

Most frequent character per script

ValueCountFrequency (%)
a1933
18.3%
l1072
 
10.2%
o849
 
8.0%
i783
 
7.4%
u679
 
6.4%
r575
 
5.4%
e563
 
5.3%
p522
 
4.9%
d466
 
4.4%
P383
 
3.6%
Other values (32)2728
25.9%
ValueCountFrequency (%)
3796
69.4%
-1389
 
25.4%
.277
 
5.1%
'10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII16024
> 99.9%
None1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
3796
23.7%
a1933
12.1%
-1389
 
8.7%
l1072
 
6.7%
o849
 
5.3%
i783
 
4.9%
u679
 
4.2%
r575
 
3.6%
e563
 
3.5%
p522
 
3.3%
Other values (35)3863
24.1%
ValueCountFrequency (%)
é1
100.0%

controle
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
nan
698 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2127
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan698
98.4%
1.011
 
1.6%
2021-02-24T13:18:38.620180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:38.669214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
nan698
98.4%
1.011
 
1.6%

Most occurring characters

ValueCountFrequency (%)
n1396
65.6%
a698
32.8%
111
 
0.5%
.11
 
0.5%
011
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2094
98.4%
Decimal Number22
 
1.0%
Other Punctuation11
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
n1396
66.7%
a698
33.3%
ValueCountFrequency (%)
111
50.0%
011
50.0%
ValueCountFrequency (%)
.11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2094
98.4%
Common33
 
1.6%

Most frequent character per script

ValueCountFrequency (%)
111
33.3%
.11
33.3%
011
33.3%
ValueCountFrequency (%)
n1396
66.7%
a698
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2127
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1396
65.6%
a698
32.8%
111
 
0.5%
.11
 
0.5%
011
 
0.5%

situation
Categorical

Distinct4
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Memory size5.7 KiB
Alignement
656 
Isolé
 
21
Groupe
 
16
Bosquet
 
15

Length

Max length10
Median length10
Mean length9.697740113
Min length5

Characters and Unicode

Total characters6866
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGroupe
2nd rowGroupe
3rd rowGroupe
4th rowBosquet
5th rowBosquet
ValueCountFrequency (%)
Alignement656
92.5%
Isolé21
 
3.0%
Groupe16
 
2.3%
Bosquet15
 
2.1%
(Missing)1
 
0.1%
2021-02-24T13:18:38.802072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:38.854704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
alignement656
92.7%
isolé21
 
3.0%
groupe16
 
2.3%
bosquet15
 
2.1%

Most occurring characters

ValueCountFrequency (%)
e1343
19.6%
n1312
19.1%
l677
9.9%
t671
9.8%
A656
9.6%
i656
9.6%
g656
9.6%
m656
9.6%
o52
 
0.8%
s36
 
0.5%
Other values (8)151
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6158
89.7%
Uppercase Letter708
 
10.3%

Most frequent character per category

ValueCountFrequency (%)
e1343
21.8%
n1312
21.3%
l677
11.0%
t671
10.9%
i656
10.7%
g656
10.7%
m656
10.7%
o52
 
0.8%
s36
 
0.6%
u31
 
0.5%
Other values (4)68
 
1.1%
ValueCountFrequency (%)
A656
92.7%
I21
 
3.0%
G16
 
2.3%
B15
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin6866
100.0%

Most frequent character per script

ValueCountFrequency (%)
e1343
19.6%
n1312
19.1%
l677
9.9%
t671
9.8%
A656
9.6%
i656
9.6%
g656
9.6%
m656
9.6%
o52
 
0.8%
s36
 
0.5%
Other values (8)151
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII6845
99.7%
None21
 
0.3%

Most frequent character per block

ValueCountFrequency (%)
e1343
19.6%
n1312
19.2%
l677
9.9%
t671
9.8%
A656
9.6%
i656
9.6%
g656
9.6%
m656
9.6%
o52
 
0.8%
s36
 
0.5%
Other values (7)130
 
1.9%
ValueCountFrequency (%)
é21
100.0%

type_sol
Categorical

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Gr
290 
MA
214 
S
100 
GR
53 
CS
 
21
Other values (4)
31 

Length

Max length2
Median length2
Mean length1.818053597
Min length1

Characters and Unicode

Total characters1289
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowP
2nd rowP
3rd rowP
4th rowG
5th rowG
ValueCountFrequency (%)
Gr290
40.9%
MA214
30.2%
S100
 
14.1%
GR53
 
7.5%
CS21
 
3.0%
G19
 
2.7%
P9
 
1.3%
TV2
 
0.3%
E1
 
0.1%
2021-02-24T13:18:38.999662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:39.056187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
gr343
48.4%
ma214
30.2%
s100
 
14.1%
cs21
 
3.0%
g19
 
2.7%
p9
 
1.3%
tv2
 
0.3%
e1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
G362
28.1%
r290
22.5%
M214
16.6%
A214
16.6%
S121
 
9.4%
R53
 
4.1%
C21
 
1.6%
P9
 
0.7%
T2
 
0.2%
V2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter999
77.5%
Lowercase Letter290
 
22.5%

Most frequent character per category

ValueCountFrequency (%)
G362
36.2%
M214
21.4%
A214
21.4%
S121
 
12.1%
R53
 
5.3%
C21
 
2.1%
P9
 
0.9%
T2
 
0.2%
V2
 
0.2%
E1
 
0.1%
ValueCountFrequency (%)
r290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1289
100.0%

Most frequent character per script

ValueCountFrequency (%)
G362
28.1%
r290
22.5%
M214
16.6%
A214
16.6%
S121
 
9.4%
R53
 
4.1%
C21
 
1.6%
P9
 
0.7%
T2
 
0.2%
V2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1289
100.0%

Most frequent character per block

ValueCountFrequency (%)
G362
28.1%
r290
22.5%
M214
16.6%
A214
16.6%
S121
 
9.4%
R53
 
4.1%
C21
 
1.6%
P9
 
0.7%
T2
 
0.2%
V2
 
0.2%

surf_permeable
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.19887165
Minimum0
Maximum100
Zeros27
Zeros (%)3.8%
Memory size5.7 KiB
2021-02-24T13:18:39.137197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1.5
Q34
95-th percentile20
Maximum100
Range100
Interquartile range (IQR)3

Descriptive statistics

Standard deviation21.03252657
Coefficient of variation (CV)2.921642112
Kurtosis15.35170648
Mean7.19887165
Median Absolute Deviation (MAD)0.5
Skewness4.126191704
Sum5104
Variance442.3671739
MonotocityNot monotonic
2021-02-24T13:18:39.206547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1318
44.9%
4228
32.2%
1.544
 
6.2%
336
 
5.1%
10034
 
4.8%
027
 
3.8%
2011
 
1.6%
510
 
1.4%
301
 
0.1%
ValueCountFrequency (%)
027
 
3.8%
1318
44.9%
1.544
 
6.2%
336
 
5.1%
4228
32.2%
ValueCountFrequency (%)
10034
 
4.8%
301
 
0.1%
2011
 
1.6%
510
 
1.4%
4228
32.2%

date_plantation
Real number (ℝ≥0)

SKEWED

Distinct13
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.530324
Minimum200
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size5.7 KiB
2021-02-24T13:18:39.275979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile1980
Q11980
median2000
Q32010
95-th percentile2018
Maximum2019
Range1819
Interquartile range (IQR)30

Descriptive statistics

Standard deviation69.05210305
Coefficient of variation (CV)0.03458605271
Kurtosis649.6371376
Mean1996.530324
Median Absolute Deviation (MAD)10
Skewness-24.94358628
Sum1415540
Variance4768.192935
MonotocityNot monotonic
2021-02-24T13:18:39.347443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1980192
27.1%
2010189
26.7%
2000183
25.8%
201544
 
6.2%
201931
 
4.4%
201831
 
4.4%
199025
 
3.5%
19605
 
0.7%
20174
 
0.6%
19502
 
0.3%
Other values (3)3
 
0.4%
ValueCountFrequency (%)
2001
 
0.1%
19502
 
0.3%
19605
 
0.7%
19701
 
0.1%
1980192
27.1%
ValueCountFrequency (%)
201931
 
4.4%
201831
 
4.4%
20174
 
0.6%
201544
 
6.2%
2010189
26.7%

classe_age
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
A
371 
JA
176 
J
155 
AM
 
7

Length

Max length2
Median length1
Mean length1.258110014
Min length1

Characters and Unicode

Total characters892
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
A371
52.3%
JA176
24.8%
J155
21.9%
AM7
 
1.0%
2021-02-24T13:18:39.742362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:39.793247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a371
52.3%
ja176
24.8%
j155
21.9%
am7
 
1.0%

Most occurring characters

ValueCountFrequency (%)
A554
62.1%
J331
37.1%
M7
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter892
100.0%

Most frequent character per category

ValueCountFrequency (%)
A554
62.1%
J331
37.1%
M7
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin892
100.0%

Most frequent character per script

ValueCountFrequency (%)
A554
62.1%
J331
37.1%
M7
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII892
100.0%

Most frequent character per block

ValueCountFrequency (%)
A554
62.1%
J331
37.1%
M7
 
0.8%

hauteur
Real number (ℝ≥0)

Distinct17
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean692.5246827
Minimum250
Maximum2500
Zeros0
Zeros (%)0.0%
Memory size5.7 KiB
2021-02-24T13:18:39.852958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile400
Q1500
median600
Q3800
95-th percentile1500
Maximum2500
Range2250
Interquartile range (IQR)300

Descriptive statistics

Standard deviation333.0232615
Coefficient of variation (CV)0.4808828765
Kurtosis9.028872581
Mean692.5246827
Median Absolute Deviation (MAD)100
Skewness2.805166205
Sum491000
Variance110904.4927
MonotocityNot monotonic
2021-02-24T13:18:39.923104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
500261
36.8%
800129
18.2%
600125
17.6%
70086
 
12.1%
40034
 
4.8%
120022
 
3.1%
150011
 
1.6%
20009
 
1.3%
18007
 
1.0%
10005
 
0.7%
Other values (7)20
 
2.8%
ValueCountFrequency (%)
2504
 
0.6%
3001
 
0.1%
40034
 
4.8%
500261
36.8%
600125
17.6%
ValueCountFrequency (%)
25003
 
0.4%
24001
 
0.1%
22003
 
0.4%
20009
1.3%
18007
1.0%

classe_hauteur
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
H2
345 
H1
300 
H3
36 
H4
 
21
H5
 
7

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1418
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowH2
2nd rowH2
3rd rowH2
4th rowH4
5th rowH4
ValueCountFrequency (%)
H2345
48.7%
H1300
42.3%
H336
 
5.1%
H421
 
3.0%
H57
 
1.0%
2021-02-24T13:18:40.082596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:40.134062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
h2345
48.7%
h1300
42.3%
h336
 
5.1%
h421
 
3.0%
h57
 
1.0%

Most occurring characters

ValueCountFrequency (%)
H709
50.0%
2345
24.3%
1300
21.2%
336
 
2.5%
421
 
1.5%
57
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter709
50.0%
Decimal Number709
50.0%

Most frequent character per category

ValueCountFrequency (%)
2345
48.7%
1300
42.3%
336
 
5.1%
421
 
3.0%
57
 
1.0%
ValueCountFrequency (%)
H709
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin709
50.0%
Common709
50.0%

Most frequent character per script

ValueCountFrequency (%)
2345
48.7%
1300
42.3%
336
 
5.1%
421
 
3.0%
57
 
1.0%
ValueCountFrequency (%)
H709
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1418
100.0%

Most frequent character per block

ValueCountFrequency (%)
H709
50.0%
2345
24.3%
1300
21.2%
336
 
2.5%
421
 
1.5%
57
 
0.5%

diametre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.5205585
Minimum12.73239545
Maximum222.8169203
Zeros0
Zeros (%)0.0%
Memory size5.7 KiB
2021-02-24T13:18:40.207824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12.73239545
5-th percentile19.09859317
Q128.64788976
median38.19718634
Q357.29577951
95-th percentile82.76057041
Maximum222.8169203
Range210.0845249
Interquartile range (IQR)28.64788976

Descriptive statistics

Standard deviation27.14145132
Coefficient of variation (CV)0.5962460086
Kurtosis12.54250063
Mean45.5205585
Median Absolute Deviation (MAD)12.73239545
Skewness2.828502729
Sum32274.07598
Variance736.6583797
MonotocityNot monotonic
2021-02-24T13:18:40.294961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
31.8309886284
 
11.8%
35.0140874869
 
9.7%
28.6478897660
 
8.5%
38.1971863452
 
7.3%
25.4647908938
 
5.4%
19.0985931733
 
4.7%
22.2816920333
 
4.7%
63.6619772433
 
4.7%
57.2957795130
 
4.2%
44.5633840729
 
4.1%
Other values (32)248
35.0%
ValueCountFrequency (%)
12.732395453
 
0.4%
15.9154943125
3.5%
19.0985931733
4.7%
22.2816920333
4.7%
25.4647908938
5.4%
ValueCountFrequency (%)
222.81692033
0.4%
190.98593174
0.6%
171.88733851
 
0.1%
152.78874541
 
0.1%
149.60564651
 
0.1%

circonference (en cm)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.50352609
Minimum20
Maximum350
Zeros0
Zeros (%)0.0%
Memory size5.7 KiB
2021-02-24T13:18:40.385706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q145
median60
Q390
95-th percentile130
Maximum350
Range330
Interquartile range (IQR)45

Descriptive statistics

Standard deviation42.63369204
Coefficient of variation (CV)0.5962460086
Kurtosis12.54250063
Mean71.50352609
Median Absolute Deviation (MAD)20
Skewness2.828502729
Sum50696
Variance1817.631697
MonotocityNot monotonic
2021-02-24T13:18:40.477033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5084
 
11.8%
5569
 
9.7%
4560
 
8.5%
6052
 
7.3%
4038
 
5.4%
10033
 
4.7%
3533
 
4.7%
3033
 
4.7%
9030
 
4.2%
7029
 
4.1%
Other values (32)248
35.0%
ValueCountFrequency (%)
203
 
0.4%
2525
3.5%
3033
4.7%
3533
4.7%
4038
5.4%
ValueCountFrequency (%)
3503
0.4%
3004
0.6%
2701
 
0.1%
2401
 
0.1%
2351
 
0.1%
Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
C2
325 
C1
276 
C3
81 
C4
 
14
C5
 
5
Other values (2)
 
8

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1418
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC2
2nd rowC2
3rd rowC2
4th rowC2
5th rowC6
ValueCountFrequency (%)
C2325
45.8%
C1276
38.9%
C381
 
11.4%
C414
 
2.0%
C55
 
0.7%
C65
 
0.7%
C73
 
0.4%
2021-02-24T13:18:40.653086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:40.706468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
c2325
45.8%
c1276
38.9%
c381
 
11.4%
c414
 
2.0%
c55
 
0.7%
c65
 
0.7%
c73
 
0.4%

Most occurring characters

ValueCountFrequency (%)
C709
50.0%
2325
22.9%
1276
 
19.5%
381
 
5.7%
414
 
1.0%
65
 
0.4%
55
 
0.4%
73
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter709
50.0%
Decimal Number709
50.0%

Most frequent character per category

ValueCountFrequency (%)
2325
45.8%
1276
38.9%
381
 
11.4%
414
 
2.0%
65
 
0.7%
55
 
0.7%
73
 
0.4%
ValueCountFrequency (%)
C709
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin709
50.0%
Common709
50.0%

Most frequent character per script

ValueCountFrequency (%)
2325
45.8%
1276
38.9%
381
 
11.4%
414
 
2.0%
65
 
0.7%
55
 
0.7%
73
 
0.4%
ValueCountFrequency (%)
C709
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1418
100.0%

Most frequent character per block

ValueCountFrequency (%)
C709
50.0%
2325
22.9%
1276
 
19.5%
381
 
5.7%
414
 
1.0%
65
 
0.4%
55
 
0.4%
73
 
0.2%

port_arbre
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
R5
319 
SL
224 
L
157 
A
 
7
RR
 
2

Length

Max length2
Median length2
Mean length1.768688293
Min length1

Characters and Unicode

Total characters1254
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSL
2nd rowSL
3rd rowSL
4th rowL
5th rowL
ValueCountFrequency (%)
R5319
45.0%
SL224
31.6%
L157
22.1%
A7
 
1.0%
RR2
 
0.3%
2021-02-24T13:18:40.867663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:40.919550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
r5319
45.0%
sl224
31.6%
l157
22.1%
a7
 
1.0%
rr2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
L381
30.4%
R323
25.8%
5319
25.4%
S224
17.9%
A7
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter935
74.6%
Decimal Number319
 
25.4%

Most frequent character per category

ValueCountFrequency (%)
L381
40.7%
R323
34.5%
S224
24.0%
A7
 
0.7%
ValueCountFrequency (%)
5319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin935
74.6%
Common319
 
25.4%

Most frequent character per script

ValueCountFrequency (%)
L381
40.7%
R323
34.5%
S224
24.0%
A7
 
0.7%
ValueCountFrequency (%)
5319
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1254
100.0%

Most frequent character per block

ValueCountFrequency (%)
L381
30.4%
R323
25.8%
5319
25.4%
S224
17.9%
A7
 
0.6%

vigueur_pousse
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
P
628 
PP
 
47
MP
 
29
D
 
5

Length

Max length2
Median length1
Mean length1.10719323
Min length1

Characters and Unicode

Total characters785
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowP
3rd rowP
4th rowP
5th rowP
ValueCountFrequency (%)
P628
88.6%
PP47
 
6.6%
MP29
 
4.1%
D5
 
0.7%
2021-02-24T13:18:41.069594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:41.121250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
p628
88.6%
pp47
 
6.6%
mp29
 
4.1%
d5
 
0.7%

Most occurring characters

ValueCountFrequency (%)
P751
95.7%
M29
 
3.7%
D5
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter785
100.0%

Most frequent character per category

ValueCountFrequency (%)
P751
95.7%
M29
 
3.7%
D5
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin785
100.0%

Most frequent character per script

ValueCountFrequency (%)
P751
95.7%
M29
 
3.7%
D5
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII785
100.0%

Most frequent character per block

ValueCountFrequency (%)
P751
95.7%
M29
 
3.7%
D5
 
0.6%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Non
699 
Oui
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2127
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non699
98.6%
Oui10
 
1.4%
2021-02-24T13:18:41.262639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:41.311449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non699
98.6%
oui10
 
1.4%

Most occurring characters

ValueCountFrequency (%)
N699
32.9%
o699
32.9%
n699
32.9%
O10
 
0.5%
u10
 
0.5%
i10
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1418
66.7%
Uppercase Letter709
33.3%

Most frequent character per category

ValueCountFrequency (%)
o699
49.3%
n699
49.3%
u10
 
0.7%
i10
 
0.7%
ValueCountFrequency (%)
N699
98.6%
O10
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin2127
100.0%

Most frequent character per script

ValueCountFrequency (%)
N699
32.9%
o699
32.9%
n699
32.9%
O10
 
0.5%
u10
 
0.5%
i10
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2127
100.0%

Most frequent character per block

ValueCountFrequency (%)
N699
32.9%
o699
32.9%
n699
32.9%
O10
 
0.5%
u10
 
0.5%
i10
 
0.5%

insecte_collet
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
Non
699 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2097
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non699
98.6%
(Missing)10
 
1.4%
2021-02-24T13:18:41.434385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:41.482386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non699
100.0%

Most occurring characters

ValueCountFrequency (%)
N699
33.3%
o699
33.3%
n699
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398
66.7%
Uppercase Letter699
33.3%

Most frequent character per category

ValueCountFrequency (%)
o699
50.0%
n699
50.0%
ValueCountFrequency (%)
N699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2097
100.0%

Most frequent character per script

ValueCountFrequency (%)
N699
33.3%
o699
33.3%
n699
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2097
100.0%

Most frequent character per block

ValueCountFrequency (%)
N699
33.3%
o699
33.3%
n699
33.3%

plaie_collet
Categorical

MISSING

Distinct7
Distinct (%)1.0%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
RCPPL
385 
RCPLS
194 
RCPLC
81 
RCPLNC
 
31
RCPLCF
 
4
Other values (2)
 
4

Length

Max length6
Median length5
Mean length5.050071531
Min length2

Characters and Unicode

Total characters3530
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowRCPLNS
2nd rowRCPPL
3rd rowRCPPL
4th rowRCPPL
5th rowRCPPL
ValueCountFrequency (%)
RCPPL385
54.3%
RCPLS194
27.4%
RCPLC81
 
11.4%
RCPLNC31
 
4.4%
RCPLCF4
 
0.6%
RCPLNS3
 
0.4%
AU1
 
0.1%
(Missing)10
 
1.4%
2021-02-24T13:18:41.629336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:41.690861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
rcppl385
55.1%
rcpls194
27.8%
rcplc81
 
11.6%
rcplnc31
 
4.4%
rcplcf4
 
0.6%
rcplns3
 
0.4%
au1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
P1083
30.7%
C814
23.1%
R698
19.8%
L698
19.8%
S197
 
5.6%
N34
 
1.0%
F4
 
0.1%
A1
 
< 0.1%
U1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3530
100.0%

Most frequent character per category

ValueCountFrequency (%)
P1083
30.7%
C814
23.1%
R698
19.8%
L698
19.8%
S197
 
5.6%
N34
 
1.0%
F4
 
0.1%
A1
 
< 0.1%
U1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin3530
100.0%

Most frequent character per script

ValueCountFrequency (%)
P1083
30.7%
C814
23.1%
R698
19.8%
L698
19.8%
S197
 
5.6%
N34
 
1.0%
F4
 
0.1%
A1
 
< 0.1%
U1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3530
100.0%

Most frequent character per block

ValueCountFrequency (%)
P1083
30.7%
C814
23.1%
R698
19.8%
L698
19.8%
S197
 
5.6%
N34
 
1.0%
F4
 
0.1%
A1
 
< 0.1%
U1
 
< 0.1%

observation_collet
Categorical

MISSING

Distinct13
Distinct (%)9.4%
Missing571
Missing (%)80.5%
Memory size5.7 KiB
Racine apparente
46 
Racine apparente
27 
Bidime
25 
Autres
11 
Choc collet
Other values (8)
23 

Length

Max length31
Median length16
Mean length13.36956522
Min length5

Characters and Unicode

Total characters1845
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st rowCépée
2nd rowCépée
3rd rowChoc collet
4th rowChoc collet
5th rowRacine de surface abimée
ValueCountFrequency (%)
Racine apparente46
 
6.5%
Racine apparente27
 
3.8%
Bidime25
 
3.5%
Autres11
 
1.6%
Choc collet6
 
0.8%
Cépée5
 
0.7%
Champignon collet 4
 
0.6%
Fructification ganoderme4
 
0.6%
Racine de surface abimée3
 
0.4%
Échaudure2
 
0.3%
Other values (3)5
 
0.7%
(Missing)571
80.5%
2021-02-24T13:18:41.852021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
racine77
31.7%
apparente73
30.0%
bidime25
 
10.3%
autres11
 
4.5%
collet11
 
4.5%
choc7
 
2.9%
cépée5
 
2.1%
surface4
 
1.6%
champignon4
 
1.6%
de4
 
1.6%
Other values (9)22
 
9.1%

Most occurring characters

ValueCountFrequency (%)
e302
16.4%
a246
13.3%
n170
9.2%
p155
8.4%
i152
8.2%
136
7.4%
c109
 
5.9%
t105
 
5.7%
r104
 
5.6%
R77
 
4.2%
Other values (19)289
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1569
85.0%
Uppercase Letter139
 
7.5%
Space Separator136
 
7.4%
Other Punctuation1
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e302
19.2%
a246
15.7%
n170
10.8%
p155
9.9%
i152
9.7%
c109
 
6.9%
t105
 
6.7%
r104
 
6.6%
d37
 
2.4%
m36
 
2.3%
Other values (9)153
9.8%
ValueCountFrequency (%)
R77
55.4%
B25
 
18.0%
C16
 
11.5%
A11
 
7.9%
F4
 
2.9%
D2
 
1.4%
P2
 
1.4%
É2
 
1.4%
ValueCountFrequency (%)
136
100.0%
ValueCountFrequency (%)
/1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1708
92.6%
Common137
 
7.4%

Most frequent character per script

ValueCountFrequency (%)
e302
17.7%
a246
14.4%
n170
10.0%
p155
9.1%
i152
8.9%
c109
 
6.4%
t105
 
6.1%
r104
 
6.1%
R77
 
4.5%
d37
 
2.2%
Other values (17)251
14.7%
ValueCountFrequency (%)
136
99.3%
/1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1830
99.2%
None15
 
0.8%

Most frequent character per block

ValueCountFrequency (%)
e302
16.5%
a246
13.4%
n170
9.3%
p155
8.5%
i152
8.3%
136
7.4%
c109
 
6.0%
t105
 
5.7%
r104
 
5.7%
R77
 
4.2%
Other values (17)274
15.0%
ValueCountFrequency (%)
é13
86.7%
É2
 
13.3%

champignon_tronc
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
Non
695 
Oui
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2097
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non695
98.0%
Oui4
 
0.6%
(Missing)10
 
1.4%
2021-02-24T13:18:42.006569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:42.055284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non695
99.4%
oui4
 
0.6%

Most occurring characters

ValueCountFrequency (%)
N695
33.1%
o695
33.1%
n695
33.1%
O4
 
0.2%
u4
 
0.2%
i4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398
66.7%
Uppercase Letter699
33.3%

Most frequent character per category

ValueCountFrequency (%)
o695
49.7%
n695
49.7%
u4
 
0.3%
i4
 
0.3%
ValueCountFrequency (%)
N695
99.4%
O4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin2097
100.0%

Most frequent character per script

ValueCountFrequency (%)
N695
33.1%
o695
33.1%
n695
33.1%
O4
 
0.2%
u4
 
0.2%
i4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2097
100.0%

Most frequent character per block

ValueCountFrequency (%)
N695
33.1%
o695
33.1%
n695
33.1%
O4
 
0.2%
u4
 
0.2%
i4
 
0.2%

insecte_tronc
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
Non
699 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2097
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non699
98.6%
(Missing)10
 
1.4%
2021-02-24T13:18:42.176935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:42.224721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non699
100.0%

Most occurring characters

ValueCountFrequency (%)
N699
33.3%
o699
33.3%
n699
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398
66.7%
Uppercase Letter699
33.3%

Most frequent character per category

ValueCountFrequency (%)
o699
50.0%
n699
50.0%
ValueCountFrequency (%)
N699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2097
100.0%

Most frequent character per script

ValueCountFrequency (%)
N699
33.3%
o699
33.3%
n699
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2097
100.0%

Most frequent character per block

ValueCountFrequency (%)
N699
33.3%
o699
33.3%
n699
33.3%

fissure_tronc
Categorical

MISSING

Distinct3
Distinct (%)0.4%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
TPF
695 
TFF
 
3
TFO
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2097
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTPF
2nd rowTPF
3rd rowTPF
4th rowTPF
5th rowTPF
ValueCountFrequency (%)
TPF695
98.0%
TFF3
 
0.4%
TFO1
 
0.1%
(Missing)10
 
1.4%
2021-02-24T13:18:42.357798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:42.407781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
tpf695
99.4%
tff3
 
0.4%
tfo1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
F702
33.5%
T699
33.3%
P695
33.1%
O1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2097
100.0%

Most frequent character per category

ValueCountFrequency (%)
F702
33.5%
T699
33.3%
P695
33.1%
O1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2097
100.0%

Most frequent character per script

ValueCountFrequency (%)
F702
33.5%
T699
33.3%
P695
33.1%
O1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2097
100.0%

Most frequent character per block

ValueCountFrequency (%)
F702
33.5%
T699
33.3%
P695
33.1%
O1
 
< 0.1%

rejet_tronc
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
Non
638 
Oui
 
61

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2097
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non638
90.0%
Oui61
 
8.6%
(Missing)10
 
1.4%
2021-02-24T13:18:42.539770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:42.588508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non638
91.3%
oui61
 
8.7%

Most occurring characters

ValueCountFrequency (%)
N638
30.4%
o638
30.4%
n638
30.4%
O61
 
2.9%
u61
 
2.9%
i61
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398
66.7%
Uppercase Letter699
33.3%

Most frequent character per category

ValueCountFrequency (%)
o638
45.6%
n638
45.6%
u61
 
4.4%
i61
 
4.4%
ValueCountFrequency (%)
N638
91.3%
O61
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Latin2097
100.0%

Most frequent character per script

ValueCountFrequency (%)
N638
30.4%
o638
30.4%
n638
30.4%
O61
 
2.9%
u61
 
2.9%
i61
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2097
100.0%

Most frequent character per block

ValueCountFrequency (%)
N638
30.4%
o638
30.4%
n638
30.4%
O61
 
2.9%
u61
 
2.9%
i61
 
2.9%

tuteurage_arbre
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Non
599 
T2:Bipode
107 
T1:Monopode
 
2
T3Tripode
 
1

Length

Max length11
Median length3
Mean length3.936530324
Min length3

Characters and Unicode

Total characters2791
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non599
84.5%
T2:Bipode107
 
15.1%
T1:Monopode2
 
0.3%
T3Tripode1
 
0.1%
2021-02-24T13:18:42.725211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:42.782545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non599
84.5%
t2:bipode107
 
15.1%
t1:monopode2
 
0.3%
t3tripode1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o713
25.5%
n601
21.5%
N599
21.5%
T111
 
4.0%
p110
 
3.9%
d110
 
3.9%
e110
 
3.9%
:109
 
3.9%
i108
 
3.9%
2107
 
3.8%
Other values (5)113
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1753
62.8%
Uppercase Letter819
29.3%
Decimal Number110
 
3.9%
Other Punctuation109
 
3.9%

Most frequent character per category

ValueCountFrequency (%)
o713
40.7%
n601
34.3%
p110
 
6.3%
d110
 
6.3%
e110
 
6.3%
i108
 
6.2%
r1
 
0.1%
ValueCountFrequency (%)
N599
73.1%
T111
 
13.6%
B107
 
13.1%
M2
 
0.2%
ValueCountFrequency (%)
2107
97.3%
12
 
1.8%
31
 
0.9%
ValueCountFrequency (%)
:109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2572
92.2%
Common219
 
7.8%

Most frequent character per script

ValueCountFrequency (%)
o713
27.7%
n601
23.4%
N599
23.3%
T111
 
4.3%
p110
 
4.3%
d110
 
4.3%
e110
 
4.3%
i108
 
4.2%
B107
 
4.2%
M2
 
0.1%
ValueCountFrequency (%)
:109
49.8%
2107
48.9%
12
 
0.9%
31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2791
100.0%

Most frequent character per block

ValueCountFrequency (%)
o713
25.5%
n601
21.5%
N599
21.5%
T111
 
4.0%
p110
 
3.9%
d110
 
3.9%
e110
 
3.9%
:109
 
3.9%
i108
 
3.9%
2107
 
3.8%
Other values (5)113
 
4.0%

canisse_arbre
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Non
534 
Oui
175 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2127
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non534
75.3%
Oui175
 
24.7%
2021-02-24T13:18:42.925274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:42.973887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non534
75.3%
oui175
 
24.7%

Most occurring characters

ValueCountFrequency (%)
N534
25.1%
o534
25.1%
n534
25.1%
O175
 
8.2%
u175
 
8.2%
i175
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1418
66.7%
Uppercase Letter709
33.3%

Most frequent character per category

ValueCountFrequency (%)
o534
37.7%
n534
37.7%
u175
 
12.3%
i175
 
12.3%
ValueCountFrequency (%)
N534
75.3%
O175
 
24.7%

Most occurring scripts

ValueCountFrequency (%)
Latin2127
100.0%

Most frequent character per script

ValueCountFrequency (%)
N534
25.1%
o534
25.1%
n534
25.1%
O175
 
8.2%
u175
 
8.2%
i175
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2127
100.0%

Most frequent character per block

ValueCountFrequency (%)
N534
25.1%
o534
25.1%
n534
25.1%
O175
 
8.2%
u175
 
8.2%
i175
 
8.2%

plaie_tronc
Categorical

MISSING

Distinct6
Distinct (%)0.9%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
TPLS
406 
TPLC
124 
TPPL
94 
TPLNC
70 
TPLCF
 
4

Length

Max length5
Median length4
Mean length4.103004292
Min length2

Characters and Unicode

Total characters2868
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTPLNC
2nd rowTPLC
3rd rowTPLCF
4th rowTPLS
5th rowTPLS
ValueCountFrequency (%)
TPLS406
57.3%
TPLC124
 
17.5%
TPPL94
 
13.3%
TPLNC70
 
9.9%
TPLCF4
 
0.6%
ZZ1
 
0.1%
(Missing)10
 
1.4%
2021-02-24T13:18:43.118423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:43.442960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
tpls406
58.1%
tplc124
 
17.7%
tppl94
 
13.4%
tplnc70
 
10.0%
tplcf4
 
0.6%
zz1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
P792
27.6%
T698
24.3%
L698
24.3%
S406
14.2%
C198
 
6.9%
N70
 
2.4%
F4
 
0.1%
Z2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2868
100.0%

Most frequent character per category

ValueCountFrequency (%)
P792
27.6%
T698
24.3%
L698
24.3%
S406
14.2%
C198
 
6.9%
N70
 
2.4%
F4
 
0.1%
Z2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2868
100.0%

Most frequent character per script

ValueCountFrequency (%)
P792
27.6%
T698
24.3%
L698
24.3%
S406
14.2%
C198
 
6.9%
N70
 
2.4%
F4
 
0.1%
Z2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2868
100.0%

Most frequent character per block

ValueCountFrequency (%)
P792
27.6%
T698
24.3%
L698
24.3%
S406
14.2%
C198
 
6.9%
N70
 
2.4%
F4
 
0.1%
Z2
 
0.1%

observation_tronc
Categorical

MISSING

Distinct8
Distinct (%)10.5%
Missing633
Missing (%)89.3%
Memory size5.7 KiB
Plaie longitudinale
26 
Échaudure
21 
Cavité ouverte
11 
Autres
Lierre
Other values (3)

Length

Max length20
Median length15
Mean length14.32894737
Min length6

Characters and Unicode

Total characters1089
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutres
2nd rowProblème de coupe
3rd rowPlaie longitudinale
4th rowProblème de coupe
5th rowProblème de coupe
ValueCountFrequency (%)
Plaie longitudinale 26
 
3.7%
Échaudure 21
 
3.0%
Cavité ouverte 11
 
1.6%
Autres7
 
1.0%
Lierre3
 
0.4%
Phellin du fruitier3
 
0.4%
Problème de coupe3
 
0.4%
Tronc torsadé2
 
0.3%
(Missing)633
89.3%
2021-02-24T13:18:43.670892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:43.736385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
plaie26
20.5%
longitudinale26
20.5%
échaudure21
16.5%
cavité11
8.7%
ouverte11
8.7%
autres7
 
5.5%
fruitier3
 
2.4%
lierre3
 
2.4%
phellin3
 
2.4%
problème3
 
2.4%
Other values (5)13
10.2%

Most occurring characters

ValueCountFrequency (%)
e123
11.3%
109
10.0%
i101
 
9.3%
u95
 
8.7%
l87
 
8.0%
a86
 
7.9%
t60
 
5.5%
r58
 
5.3%
n57
 
5.2%
d55
 
5.1%
Other values (18)258
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter904
83.0%
Space Separator109
 
10.0%
Uppercase Letter76
 
7.0%

Most frequent character per category

ValueCountFrequency (%)
e123
13.6%
i101
11.2%
u95
10.5%
l87
9.6%
a86
9.5%
t60
6.6%
r58
6.4%
n57
6.3%
d55
6.1%
o47
 
5.2%
Other values (11)135
14.9%
ValueCountFrequency (%)
P32
42.1%
É21
27.6%
C11
 
14.5%
A7
 
9.2%
L3
 
3.9%
T2
 
2.6%
ValueCountFrequency (%)
109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin980
90.0%
Common109
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
e123
12.6%
i101
10.3%
u95
9.7%
l87
8.9%
a86
8.8%
t60
 
6.1%
r58
 
5.9%
n57
 
5.8%
d55
 
5.6%
o47
 
4.8%
Other values (17)211
21.5%
ValueCountFrequency (%)
109
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1052
96.6%
None37
 
3.4%

Most frequent character per block

ValueCountFrequency (%)
e123
11.7%
109
10.4%
i101
9.6%
u95
9.0%
l87
 
8.3%
a86
 
8.2%
t60
 
5.7%
r58
 
5.5%
n57
 
5.4%
d55
 
5.2%
Other values (15)221
21.0%
ValueCountFrequency (%)
É21
56.8%
é13
35.1%
è3
 
8.1%

champignon_houppier
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
Non
691 
Oui
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2097
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non691
97.5%
Oui8
 
1.1%
(Missing)10
 
1.4%
2021-02-24T13:18:43.903118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:43.951910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non691
98.9%
oui8
 
1.1%

Most occurring characters

ValueCountFrequency (%)
N691
33.0%
o691
33.0%
n691
33.0%
O8
 
0.4%
u8
 
0.4%
i8
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398
66.7%
Uppercase Letter699
33.3%

Most frequent character per category

ValueCountFrequency (%)
o691
49.4%
n691
49.4%
u8
 
0.6%
i8
 
0.6%
ValueCountFrequency (%)
N691
98.9%
O8
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2097
100.0%

Most frequent character per script

ValueCountFrequency (%)
N691
33.0%
o691
33.0%
n691
33.0%
O8
 
0.4%
u8
 
0.4%
i8
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2097
100.0%

Most frequent character per block

ValueCountFrequency (%)
N691
33.0%
o691
33.0%
n691
33.0%
O8
 
0.4%
u8
 
0.4%
i8
 
0.4%

insecte_houppier
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
Non
699 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2097
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non699
98.6%
(Missing)10
 
1.4%
2021-02-24T13:18:44.073930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:44.121920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non699
100.0%

Most occurring characters

ValueCountFrequency (%)
N699
33.3%
o699
33.3%
n699
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398
66.7%
Uppercase Letter699
33.3%

Most frequent character per category

ValueCountFrequency (%)
o699
50.0%
n699
50.0%
ValueCountFrequency (%)
N699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2097
100.0%

Most frequent character per script

ValueCountFrequency (%)
N699
33.3%
o699
33.3%
n699
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2097
100.0%

Most frequent character per block

ValueCountFrequency (%)
N699
33.3%
o699
33.3%
n699
33.3%

fissure_houppier
Categorical

MISSING

Distinct3
Distinct (%)0.4%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
HPF
695 
HFO
 
2
HFF
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2796
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHPF
2nd rowHPF
3rd rowHPF
4th rowHPF
5th rowHPF
ValueCountFrequency (%)
HPF 695
98.0%
HFO 2
 
0.3%
HFF 2
 
0.3%
(Missing)10
 
1.4%
2021-02-24T13:18:44.255685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:44.305658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hpf695
99.4%
hff2
 
0.3%
hfo2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
F701
25.1%
H699
25.0%
699
25.0%
P695
24.9%
O2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2097
75.0%
Space Separator699
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
F701
33.4%
H699
33.3%
P695
33.1%
O2
 
0.1%
ValueCountFrequency (%)
699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2097
75.0%
Common699
 
25.0%

Most frequent character per script

ValueCountFrequency (%)
F701
33.4%
H699
33.3%
P695
33.1%
O2
 
0.1%
ValueCountFrequency (%)
699
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2796
100.0%

Most frequent character per block

ValueCountFrequency (%)
F701
25.1%
H699
25.0%
699
25.0%
P695
24.9%
O2
 
0.1%

ecorce_incluse_houppier
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
Non
695 
Oui
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2097
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non695
98.0%
Oui4
 
0.6%
(Missing)10
 
1.4%
2021-02-24T13:18:44.443180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:44.492104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non695
99.4%
oui4
 
0.6%

Most occurring characters

ValueCountFrequency (%)
N695
33.1%
o695
33.1%
n695
33.1%
O4
 
0.2%
u4
 
0.2%
i4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398
66.7%
Uppercase Letter699
33.3%

Most frequent character per category

ValueCountFrequency (%)
o695
49.7%
n695
49.7%
u4
 
0.3%
i4
 
0.3%
ValueCountFrequency (%)
N695
99.4%
O4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin2097
100.0%

Most frequent character per script

ValueCountFrequency (%)
N695
33.1%
o695
33.1%
n695
33.1%
O4
 
0.2%
u4
 
0.2%
i4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2097
100.0%

Most frequent character per block

ValueCountFrequency (%)
N695
33.1%
o695
33.1%
n695
33.1%
O4
 
0.2%
u4
 
0.2%
i4
 
0.2%

bois_mort_houppier
Categorical

MISSING

Distinct3
Distinct (%)0.4%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
HPBM
666 
HBMI
 
26
HBMU
 
7

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3495
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHBMI
2nd rowHBMI
3rd rowHBMI
4th rowHBMI
5th rowHBMI
ValueCountFrequency (%)
HPBM 666
93.9%
HBMI 26
 
3.7%
HBMU 7
 
1.0%
(Missing)10
 
1.4%
2021-02-24T13:18:44.627524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:44.677557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hpbm666
95.3%
hbmi26
 
3.7%
hbmu7
 
1.0%

Most occurring characters

ValueCountFrequency (%)
H699
20.0%
B699
20.0%
M699
20.0%
699
20.0%
P666
19.1%
I26
 
0.7%
U7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2796
80.0%
Space Separator699
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
H699
25.0%
B699
25.0%
M699
25.0%
P666
23.8%
I26
 
0.9%
U7
 
0.3%
ValueCountFrequency (%)
699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2796
80.0%
Common699
 
20.0%

Most frequent character per script

ValueCountFrequency (%)
H699
25.0%
B699
25.0%
M699
25.0%
P666
23.8%
I26
 
0.9%
U7
 
0.3%
ValueCountFrequency (%)
699
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3495
100.0%

Most frequent character per block

ValueCountFrequency (%)
H699
20.0%
B699
20.0%
M699
20.0%
699
20.0%
P666
19.1%
I26
 
0.7%
U7
 
0.2%

plaie_houppier
Categorical

MISSING

Distinct5
Distinct (%)0.7%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
HPLS
531 
HPPL
126 
HPLNC
 
37
HPLC
 
3
ZZ
 
2

Length

Max length5
Median length4
Mean length4.0472103
Min length2

Characters and Unicode

Total characters2829
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHPLC
2nd rowHPLC
3rd rowHPLC
4th rowHPLS
5th rowHPLS
ValueCountFrequency (%)
HPLS531
74.9%
HPPL126
 
17.8%
HPLNC37
 
5.2%
HPLC3
 
0.4%
ZZ2
 
0.3%
(Missing)10
 
1.4%
2021-02-24T13:18:44.815330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:44.871282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hpls531
76.0%
hppl126
 
18.0%
hplnc37
 
5.3%
hplc3
 
0.4%
zz2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
P823
29.1%
H697
24.6%
L697
24.6%
S531
18.8%
C40
 
1.4%
N37
 
1.3%
Z4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2829
100.0%

Most frequent character per category

ValueCountFrequency (%)
P823
29.1%
H697
24.6%
L697
24.6%
S531
18.8%
C40
 
1.4%
N37
 
1.3%
Z4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2829
100.0%

Most frequent character per script

ValueCountFrequency (%)
P823
29.1%
H697
24.6%
L697
24.6%
S531
18.8%
C40
 
1.4%
N37
 
1.3%
Z4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2829
100.0%

Most frequent character per block

ValueCountFrequency (%)
P823
29.1%
H697
24.6%
L697
24.6%
S531
18.8%
C40
 
1.4%
N37
 
1.3%
Z4
 
0.1%

observation_houppier
Categorical

MISSING

Distinct17
Distinct (%)12.4%
Missing572
Missing (%)80.7%
Memory size5.7 KiB
Affaiblissement physiologique important
27 
Axe 3 Mort 2M Nord
20 
Branches cassées
16 
Déchirure
10 
Cavité ouverte
Other values (12)
56 

Length

Max length44
Median length18
Mean length21.52554745
Min length6

Characters and Unicode

Total characters2949
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAxe 3 Mort 2M Nord
2nd rowAxe 3 Mort 2M Nord
3rd rowTuteur de formation
4th rowTuteur de formation
5th rowTuteur de formation
ValueCountFrequency (%)
Affaiblissement physiologique important27
 
3.8%
Axe 3 Mort 2M Nord20
 
2.8%
Branches cassées 16
 
2.3%
Déchirure 10
 
1.4%
Cavité ouverte8
 
1.1%
Tuteur de formation7
 
1.0%
Échaudure7
 
1.0%
Phellin du fruitier7
 
1.0%
Lierre6
 
0.8%
Axe2 dépassement dans la résidence mitoyenne5
 
0.7%
Other values (7)24
 
3.4%
(Missing)572
80.7%
2021-02-24T13:18:45.034465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
physiologique27
 
7.0%
affaiblissement27
 
7.0%
important27
 
7.0%
2m22
 
5.7%
320
 
5.2%
axe20
 
5.2%
mort20
 
5.2%
nord20
 
5.2%
branches19
 
5.0%
cassées16
 
4.2%
Other values (32)165
43.1%

Most occurring characters

ValueCountFrequency (%)
e294
 
10.0%
272
 
9.2%
i236
 
8.0%
r196
 
6.6%
s185
 
6.3%
t177
 
6.0%
o172
 
5.8%
a151
 
5.1%
n139
 
4.7%
u121
 
4.1%
Other values (31)1006
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2425
82.2%
Space Separator272
 
9.2%
Uppercase Letter203
 
6.9%
Decimal Number49
 
1.7%

Most frequent character per category

ValueCountFrequency (%)
e294
12.1%
i236
 
9.7%
r196
 
8.1%
s185
 
7.6%
t177
 
7.3%
o172
 
7.1%
a151
 
6.2%
n139
 
5.7%
u121
 
5.0%
l93
 
3.8%
Other values (13)661
27.3%
ValueCountFrequency (%)
A55
27.1%
M42
20.7%
N20
 
9.9%
B20
 
9.9%
É11
 
5.4%
D10
 
4.9%
P9
 
4.4%
C8
 
3.9%
T7
 
3.4%
L6
 
3.0%
Other values (5)15
 
7.4%
ValueCountFrequency (%)
229
59.2%
320
40.8%
ValueCountFrequency (%)
272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2628
89.1%
Common321
 
10.9%

Most frequent character per script

ValueCountFrequency (%)
e294
 
11.2%
i236
 
9.0%
r196
 
7.5%
s185
 
7.0%
t177
 
6.7%
o172
 
6.5%
a151
 
5.7%
n139
 
5.3%
u121
 
4.6%
l93
 
3.5%
Other values (28)864
32.9%
ValueCountFrequency (%)
272
84.7%
229
 
9.0%
320
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2880
97.7%
None69
 
2.3%

Most frequent character per block

ValueCountFrequency (%)
e294
 
10.2%
272
 
9.4%
i236
 
8.2%
r196
 
6.8%
s185
 
6.4%
t177
 
6.1%
o172
 
6.0%
a151
 
5.2%
n139
 
4.8%
u121
 
4.2%
Other values (29)937
32.5%
ValueCountFrequency (%)
é58
84.1%
É11
 
15.9%
Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
1.0
507 
2.0
136 
3.0
 
47
nan
 
10
4.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2127
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0507
71.5%
2.0136
 
19.2%
3.047
 
6.6%
nan10
 
1.4%
4.09
 
1.3%
2021-02-24T13:18:45.196762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:45.248289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0507
71.5%
2.0136
 
19.2%
3.047
 
6.6%
nan10
 
1.4%
4.09
 
1.3%

Most occurring characters

ValueCountFrequency (%)
.699
32.9%
0699
32.9%
1507
23.8%
2136
 
6.4%
347
 
2.2%
n20
 
0.9%
a10
 
0.5%
49
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1398
65.7%
Other Punctuation699
32.9%
Lowercase Letter30
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
0699
50.0%
1507
36.3%
2136
 
9.7%
347
 
3.4%
49
 
0.6%
ValueCountFrequency (%)
n20
66.7%
a10
33.3%
ValueCountFrequency (%)
.699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2097
98.6%
Latin30
 
1.4%

Most frequent character per script

ValueCountFrequency (%)
.699
33.3%
0699
33.3%
1507
24.2%
2136
 
6.5%
347
 
2.2%
49
 
0.4%
ValueCountFrequency (%)
n20
66.7%
a10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2127
100.0%

Most frequent character per block

ValueCountFrequency (%)
.699
32.9%
0699
32.9%
1507
23.8%
2136
 
6.4%
347
 
2.2%
n20
 
0.9%
a10
 
0.5%
49
 
0.4%

contrainte
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
Non
673 
Oui
 
26

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2097
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon
2nd rowNon
3rd rowNon
4th rowNon
5th rowNon
ValueCountFrequency (%)
Non673
94.9%
Oui26
 
3.7%
(Missing)10
 
1.4%
2021-02-24T13:18:45.394353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:45.443916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non673
96.3%
oui26
 
3.7%

Most occurring characters

ValueCountFrequency (%)
N673
32.1%
o673
32.1%
n673
32.1%
O26
 
1.2%
u26
 
1.2%
i26
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398
66.7%
Uppercase Letter699
33.3%

Most frequent character per category

ValueCountFrequency (%)
o673
48.1%
n673
48.1%
u26
 
1.9%
i26
 
1.9%
ValueCountFrequency (%)
N673
96.3%
O26
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Latin2097
100.0%

Most frequent character per script

ValueCountFrequency (%)
N673
32.1%
o673
32.1%
n673
32.1%
O26
 
1.2%
u26
 
1.2%
i26
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2097
100.0%

Most frequent character per block

ValueCountFrequency (%)
N673
32.1%
o673
32.1%
n673
32.1%
O26
 
1.2%
u26
 
1.2%
i26
 
1.2%

classification_diagnostic
Categorical

MISSING

Distinct5
Distinct (%)0.7%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
C2
398 
C1
237 
C3
43 
C4
 
11
C5
 
10

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1398
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC2
2nd rowC2
3rd rowC2
4th rowC2
5th rowC2
ValueCountFrequency (%)
C2398
56.1%
C1237
33.4%
C343
 
6.1%
C411
 
1.6%
C510
 
1.4%
(Missing)10
 
1.4%
2021-02-24T13:18:45.601568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:45.685870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
c2398
56.9%
c1237
33.9%
c343
 
6.2%
c411
 
1.6%
c510
 
1.4%

Most occurring characters

ValueCountFrequency (%)
C699
50.0%
2398
28.5%
1237
 
17.0%
343
 
3.1%
411
 
0.8%
510
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter699
50.0%
Decimal Number699
50.0%

Most frequent character per category

ValueCountFrequency (%)
2398
56.9%
1237
33.9%
343
 
6.2%
411
 
1.6%
510
 
1.4%
ValueCountFrequency (%)
C699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin699
50.0%
Common699
50.0%

Most frequent character per script

ValueCountFrequency (%)
2398
56.9%
1237
33.9%
343
 
6.2%
411
 
1.6%
510
 
1.4%
ValueCountFrequency (%)
C699
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1398
100.0%

Most frequent character per block

ValueCountFrequency (%)
C699
50.0%
2398
28.5%
1237
 
17.0%
343
 
3.1%
411
 
0.8%
510
 
0.7%

date_diagnostic
Categorical

MISSING

Distinct22
Distinct (%)3.1%
Missing10
Missing (%)1.4%
Memory size5.7 KiB
2020/11/24
86 
2020/11/16
78 
2020/11/23
73 
2020/11/25
73 
2020/11/18
69 
Other values (17)
320 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6990
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row2020/10/22
2nd row2020/10/22
3rd row2020/10/22
4th row2020/10/07
5th row2020/10/07
ValueCountFrequency (%)
2020/11/2486
12.1%
2020/11/1678
11.0%
2020/11/2373
10.3%
2020/11/2573
10.3%
2020/11/1869
9.7%
2020/10/2254
7.6%
2020/10/2144
 
6.2%
2020/12/1637
 
5.2%
2020/11/1333
 
4.7%
2020/11/1931
 
4.4%
Other values (12)121
17.1%
2021-02-24T13:18:45.845680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020/11/2486
12.3%
2020/11/1678
11.2%
2020/11/2373
10.4%
2020/11/2573
10.4%
2020/11/1869
9.9%
2020/10/2254
7.7%
2020/10/2144
 
6.3%
2020/12/1637
 
5.3%
2020/11/1333
 
4.7%
2020/11/1931
 
4.4%
Other values (12)121
17.3%

Most occurring characters

ValueCountFrequency (%)
21896
27.1%
01616
23.1%
11510
21.6%
/1398
20.0%
6123
 
1.8%
3119
 
1.7%
486
 
1.2%
585
 
1.2%
879
 
1.1%
961
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5592
80.0%
Other Punctuation1398
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
21896
33.9%
01616
28.9%
11510
27.0%
6123
 
2.2%
3119
 
2.1%
486
 
1.5%
585
 
1.5%
879
 
1.4%
961
 
1.1%
717
 
0.3%
ValueCountFrequency (%)
/1398
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6990
100.0%

Most frequent character per script

ValueCountFrequency (%)
21896
27.1%
01616
23.1%
11510
21.6%
/1398
20.0%
6123
 
1.8%
3119
 
1.7%
486
 
1.2%
585
 
1.2%
879
 
1.1%
961
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII6990
100.0%

Most frequent character per block

ValueCountFrequency (%)
21896
27.1%
01616
23.1%
11510
21.6%
/1398
20.0%
6123
 
1.8%
3119
 
1.7%
486
 
1.2%
585
 
1.2%
879
 
1.1%
961
 
0.9%

prescription_1
Categorical

MISSING

Distinct15
Distinct (%)3.1%
Missing230
Missing (%)32.4%
Memory size5.7 KiB
tfo
280 
RC
91 
RT
29 
rg
 
17
ab
 
13
Other values (10)
49 

Length

Max length4
Median length3
Mean length2.680584551
Min length2

Characters and Unicode

Total characters1284
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowtdg
2nd rowtdg
3rd rowtdg
4th rowtsbm
5th rowtsbm
ValueCountFrequency (%)
tfo280
39.5%
RC91
 
12.8%
RT29
 
4.1%
rg17
 
2.4%
ab13
 
1.8%
tdg10
 
1.4%
trd8
 
1.1%
tsbm7
 
1.0%
tac7
 
1.0%
tr5
 
0.7%
Other values (5)12
 
1.7%
(Missing)230
32.4%
2021-02-24T13:18:46.008802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tfo280
58.5%
rc91
 
19.0%
rt29
 
6.1%
rg17
 
3.5%
ab13
 
2.7%
tdg10
 
2.1%
trd8
 
1.7%
tsbm7
 
1.5%
tac7
 
1.5%
tr5
 
1.0%
Other values (5)12
 
2.5%

Most occurring characters

ValueCountFrequency (%)
t320
24.9%
f280
21.8%
o280
21.8%
R120
 
9.3%
C91
 
7.1%
T33
 
2.6%
r33
 
2.6%
g29
 
2.3%
a26
 
2.0%
b20
 
1.6%
Other values (7)52
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1040
81.0%
Uppercase Letter244
 
19.0%

Most frequent character per category

ValueCountFrequency (%)
t320
30.8%
f280
26.9%
o280
26.9%
r33
 
3.2%
g29
 
2.8%
a26
 
2.5%
b20
 
1.9%
d18
 
1.7%
s10
 
1.0%
m7
 
0.7%
Other values (4)17
 
1.6%
ValueCountFrequency (%)
R120
49.2%
C91
37.3%
T33
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1284
100.0%

Most frequent character per script

ValueCountFrequency (%)
t320
24.9%
f280
21.8%
o280
21.8%
R120
 
9.3%
C91
 
7.1%
T33
 
2.6%
r33
 
2.6%
g29
 
2.3%
a26
 
2.0%
b20
 
1.6%
Other values (7)52
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1284
100.0%

Most frequent character per block

ValueCountFrequency (%)
t320
24.9%
f280
21.8%
o280
21.8%
R120
 
9.3%
C91
 
7.1%
T33
 
2.6%
r33
 
2.6%
g29
 
2.3%
a26
 
2.0%
b20
 
1.6%
Other values (7)52
 
4.0%

prescription_2
Categorical

MISSING

Distinct9
Distinct (%)5.4%
Missing542
Missing (%)76.4%
Memory size5.7 KiB
RT
99 
RC
61 
tsbm
 
1
Tpa
 
1
rg
 
1
Other values (4)
 
4

Length

Max length4
Median length2
Mean length2.02994012
Min length2

Characters and Unicode

Total characters339
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)4.2%

Sample

1st rowtsbm
2nd rowtdg
3rd rowRC
4th rowRT
5th rowRT
ValueCountFrequency (%)
RT99
 
14.0%
RC61
 
8.6%
tsbm1
 
0.1%
Tpa1
 
0.1%
rg1
 
0.1%
trg1
 
0.1%
sl1
 
0.1%
au1
 
0.1%
tdg1
 
0.1%
(Missing)542
76.4%
2021-02-24T13:18:46.179328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:46.244127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
rt99
59.3%
rc61
36.5%
sl1
 
0.6%
tpa1
 
0.6%
tdg1
 
0.6%
trg1
 
0.6%
rg1
 
0.6%
au1
 
0.6%
tsbm1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
R160
47.2%
T100
29.5%
C61
 
18.0%
t3
 
0.9%
g3
 
0.9%
s2
 
0.6%
r2
 
0.6%
a2
 
0.6%
b1
 
0.3%
m1
 
0.3%
Other values (4)4
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter321
94.7%
Lowercase Letter18
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
t3
16.7%
g3
16.7%
s2
11.1%
r2
11.1%
a2
11.1%
b1
 
5.6%
m1
 
5.6%
d1
 
5.6%
l1
 
5.6%
p1
 
5.6%
ValueCountFrequency (%)
R160
49.8%
T100
31.2%
C61
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Latin339
100.0%

Most frequent character per script

ValueCountFrequency (%)
R160
47.2%
T100
29.5%
C61
 
18.0%
t3
 
0.9%
g3
 
0.9%
s2
 
0.6%
r2
 
0.6%
a2
 
0.6%
b1
 
0.3%
m1
 
0.3%
Other values (4)4
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII339
100.0%

Most frequent character per block

ValueCountFrequency (%)
R160
47.2%
T100
29.5%
C61
 
18.0%
t3
 
0.9%
g3
 
0.9%
s2
 
0.6%
r2
 
0.6%
a2
 
0.6%
b1
 
0.3%
m1
 
0.3%
Other values (4)4
 
1.2%

prescription_3
Categorical

MISSING

Distinct2
Distinct (%)13.3%
Missing694
Missing (%)97.9%
Memory size5.7 KiB
rg
RC

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters30
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRC
2nd rowRC
3rd rowRC
4th rowRC
5th rowRC
ValueCountFrequency (%)
rg8
 
1.1%
RC7
 
1.0%
(Missing)694
97.9%
2021-02-24T13:18:46.404881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:46.454004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
rg8
53.3%
rc7
46.7%

Most occurring characters

ValueCountFrequency (%)
r8
26.7%
g8
26.7%
R7
23.3%
C7
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16
53.3%
Uppercase Letter14
46.7%

Most frequent character per category

ValueCountFrequency (%)
R7
50.0%
C7
50.0%
ValueCountFrequency (%)
r8
50.0%
g8
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin30
100.0%

Most frequent character per script

ValueCountFrequency (%)
r8
26.7%
g8
26.7%
R7
23.3%
C7
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30
100.0%

Most frequent character per block

ValueCountFrequency (%)
r8
26.7%
g8
26.7%
R7
23.3%
C7
23.3%

observation_travaux
Categorical

MISSING

Distinct16
Distinct (%)17.8%
Missing619
Missing (%)87.3%
Memory size5.7 KiB
coté cimetière ligne électrique
49 
accès difficile
12 
retirer canisse
Rehaussement de la voute
Arbre jeune a faible avenir voir évolution dans le temps
 
2
Other values (11)
11 

Length

Max length63
Median length31
Mean length27.66666667
Min length11

Characters and Unicode

Total characters2490
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)12.2%

Sample

1st rowcoté cimetière ligne électrique
2nd rowcoté cimetière ligne électrique
3rd rowcoté cimetière ligne électrique
4th rowcoté cimetière ligne électrique
5th rowcoté cimetière ligne électrique
ValueCountFrequency (%)
coté cimetière ligne électrique49
 
6.9%
accès difficile12
 
1.7%
retirer canisse8
 
1.1%
Rehaussement de la voute8
 
1.1%
Arbre jeune a faible avenir voir évolution dans le temps2
 
0.3%
branches mortes au dessus du trottoir1
 
0.1%
prévoir remplacement1
 
0.1%
Branches mitoyennes genantes1
 
0.1%
réduction de 2m de la charpentière S par rapport a la fissure1
 
0.1%
Diminution façade charpentière Est1
 
0.1%
Other values (6)6
 
0.8%
(Missing)619
87.3%
2021-02-24T13:18:46.596277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
électrique49
14.3%
ligne49
14.3%
coté49
14.3%
cimetière49
14.3%
difficile12
 
3.5%
accès12
 
3.5%
de11
 
3.2%
la10
 
2.9%
rehaussement8
 
2.3%
retirer8
 
2.3%
Other values (48)86
25.1%

Most occurring characters

ValueCountFrequency (%)
e371
14.9%
i278
11.2%
253
10.2%
c205
 
8.2%
t201
 
8.1%
r170
 
6.8%
l130
 
5.2%
é107
 
4.3%
n94
 
3.8%
u80
 
3.2%
Other values (27)601
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2214
88.9%
Space Separator253
 
10.2%
Uppercase Letter20
 
0.8%
Decimal Number3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e371
16.8%
i278
12.6%
c205
9.3%
t201
9.1%
r170
 
7.7%
l130
 
5.9%
é107
 
4.8%
n94
 
4.2%
u80
 
3.6%
o78
 
3.5%
Other values (15)500
22.6%
ValueCountFrequency (%)
R8
40.0%
E3
 
15.0%
B2
 
10.0%
A2
 
10.0%
P1
 
5.0%
O1
 
5.0%
D1
 
5.0%
S1
 
5.0%
T1
 
5.0%
ValueCountFrequency (%)
22
66.7%
81
33.3%
ValueCountFrequency (%)
253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2234
89.7%
Common256
 
10.3%

Most frequent character per script

ValueCountFrequency (%)
e371
16.6%
i278
12.4%
c205
 
9.2%
t201
 
9.0%
r170
 
7.6%
l130
 
5.8%
é107
 
4.8%
n94
 
4.2%
u80
 
3.6%
o78
 
3.5%
Other values (24)520
23.3%
ValueCountFrequency (%)
253
98.8%
22
 
0.8%
81
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2316
93.0%
None174
 
7.0%

Most frequent character per block

ValueCountFrequency (%)
e371
16.0%
i278
12.0%
253
10.9%
c205
8.9%
t201
8.7%
r170
 
7.3%
l130
 
5.6%
n94
 
4.1%
u80
 
3.5%
o78
 
3.4%
Other values (24)456
19.7%
ValueCountFrequency (%)
é107
61.5%
è66
37.9%
ç1
 
0.6%

type_delai_1
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)12.5%
Missing701
Missing (%)98.9%
Memory size5.7 KiB
b

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb
2nd rowb
3rd rowb
4th rowb
5th rowb
ValueCountFrequency (%)
b8
 
1.1%
(Missing)701
98.9%
2021-02-24T13:18:46.744304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:46.792413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
b8
100.0%

Most occurring characters

ValueCountFrequency (%)
b8
100.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8
100.0%

Most frequent character per category

ValueCountFrequency (%)
b8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8
100.0%

Most frequent character per script

ValueCountFrequency (%)
b8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8
100.0%

Most frequent character per block

ValueCountFrequency (%)
b8
100.0%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
nan
701 
2022.0
 
8

Length

Max length6
Median length3
Mean length3.033850494
Min length3

Characters and Unicode

Total characters2151
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan701
98.9%
2022.08
 
1.1%
2021-02-24T13:18:46.921819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:46.975960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
nan701
98.9%
2022.08
 
1.1%

Most occurring characters

ValueCountFrequency (%)
n1402
65.2%
a701
32.6%
224
 
1.1%
016
 
0.7%
.8
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2103
97.8%
Decimal Number40
 
1.9%
Other Punctuation8
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
n1402
66.7%
a701
33.3%
ValueCountFrequency (%)
224
60.0%
016
40.0%
ValueCountFrequency (%)
.8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2103
97.8%
Common48
 
2.2%

Most frequent character per script

ValueCountFrequency (%)
224
50.0%
016
33.3%
.8
 
16.7%
ValueCountFrequency (%)
n1402
66.7%
a701
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2151
100.0%

Most frequent character per block

ValueCountFrequency (%)
n1402
65.2%
a701
32.6%
224
 
1.1%
016
 
0.7%
.8
 
0.4%

type_delai_2
Categorical

MISSING

Distinct2
Distinct (%)0.4%
Missing230
Missing (%)32.4%
Memory size5.7 KiB
b
453 
a
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters479
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb
2nd rowb
3rd rowb
4th rowb
5th rowb
ValueCountFrequency (%)
b453
63.9%
a26
 
3.7%
(Missing)230
32.4%
2021-02-24T13:18:47.091337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:47.137822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
b453
94.6%
a26
 
5.4%

Most occurring characters

ValueCountFrequency (%)
b453
94.6%
a26
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter479
100.0%

Most frequent character per category

ValueCountFrequency (%)
b453
94.6%
a26
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin479
100.0%

Most frequent character per script

ValueCountFrequency (%)
b453
94.6%
a26
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII479
100.0%

Most frequent character per block

ValueCountFrequency (%)
b453
94.6%
a26
 
5.4%

delai_preconisation_2
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)3.0%
Missing676
Missing (%)95.3%
Memory size5.7 KiB
a3
33 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters66
Distinct characters2
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa3
2nd rowa3
3rd rowa3
4th rowa3
5th rowa3
ValueCountFrequency (%)
a333
 
4.7%
(Missing)676
95.3%
2021-02-24T13:18:47.258023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:47.304773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a333
100.0%

Most occurring characters

ValueCountFrequency (%)
a33
50.0%
333
50.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter33
50.0%
Decimal Number33
50.0%

Most frequent character per category

ValueCountFrequency (%)
a33
100.0%
ValueCountFrequency (%)
333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin33
50.0%
Common33
50.0%

Most frequent character per script

ValueCountFrequency (%)
a33
100.0%
ValueCountFrequency (%)
333
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII66
100.0%

Most frequent character per block

ValueCountFrequency (%)
a33
50.0%
333
50.0%
Distinct3
Distinct (%)0.7%
Missing256
Missing (%)36.1%
Memory size5.7 KiB
b2
439 
b1
 
8
b4
 
6

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters906
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb4
2nd rowb4
3rd rowb4
4th rowb4
5th rowb4
ValueCountFrequency (%)
b2439
61.9%
b18
 
1.1%
b46
 
0.8%
(Missing)256
36.1%
2021-02-24T13:18:47.425432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:47.472212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
b2439
96.9%
b18
 
1.8%
b46
 
1.3%

Most occurring characters

ValueCountFrequency (%)
b453
50.0%
2439
48.5%
18
 
0.9%
46
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter453
50.0%
Decimal Number453
50.0%

Most frequent character per category

ValueCountFrequency (%)
2439
96.9%
18
 
1.8%
46
 
1.3%
ValueCountFrequency (%)
b453
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin453
50.0%
Common453
50.0%

Most frequent character per script

ValueCountFrequency (%)
2439
96.9%
18
 
1.8%
46
 
1.3%
ValueCountFrequency (%)
b453
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII906
100.0%

Most frequent character per block

ValueCountFrequency (%)
b453
50.0%
2439
48.5%
18
 
0.9%
46
 
0.7%
Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2022.0
428 
nan
255 
2021.0
 
22
2023.0
 
4

Length

Max length6
Median length6
Mean length4.921015515
Min length3

Characters and Unicode

Total characters3489
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022.0
2nd row2022.0
3rd row2022.0
4th row2022.0
5th row2022.0
ValueCountFrequency (%)
2022.0428
60.4%
nan255
36.0%
2021.022
 
3.1%
2023.04
 
0.6%
2021-02-24T13:18:47.606109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T13:18:47.658107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2022.0428
60.4%
nan255
36.0%
2021.022
 
3.1%
2023.04
 
0.6%

Most occurring characters

ValueCountFrequency (%)
21336
38.3%
0908
26.0%
n510
 
14.6%
.454
 
13.0%
a255
 
7.3%
122
 
0.6%
34
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2270
65.1%
Lowercase Letter765
 
21.9%
Other Punctuation454
 
13.0%

Most frequent character per category

ValueCountFrequency (%)
21336
58.9%
0908
40.0%
122
 
1.0%
34
 
0.2%
ValueCountFrequency (%)
n510
66.7%
a255
33.3%
ValueCountFrequency (%)
.454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2724
78.1%
Latin765
 
21.9%

Most frequent character per script

ValueCountFrequency (%)
21336
49.0%
0908
33.3%
.454
 
16.7%
122
 
0.8%
34
 
0.1%
ValueCountFrequency (%)
n510
66.7%
a255
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3489
100.0%

Most frequent character per block

ValueCountFrequency (%)
21336
38.3%
0908
26.0%
n510
 
14.6%
.454
 
13.0%
a255
 
7.3%
122
 
0.6%
34
 
0.1%

reference_photo
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct231
Distinct (%)100.0%
Missing478
Missing (%)67.4%
Memory size5.7 KiB
454/455
 
1
260
 
1
222
 
1
326
 
1
226
 
1
Other values (226)
226 

Length

Max length23
Median length3
Mean length3.748917749
Min length3

Characters and Unicode

Total characters866
Distinct characters14
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique231 ?
Unique (%)100.0%

Sample

1st row259
2nd row209
3rd row210
4th row254
5th row255
ValueCountFrequency (%)
454/4551
 
0.1%
2601
 
0.1%
2221
 
0.1%
3261
 
0.1%
2261
 
0.1%
5081
 
0.1%
429/4301
 
0.1%
4761
 
0.1%
2731
 
0.1%
4741
 
0.1%
Other values (221)221
31.2%
(Missing)478
67.4%
2021-02-24T13:18:47.835611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5261
 
0.4%
2181
 
0.4%
9511
 
0.4%
2221
 
0.4%
3261
 
0.4%
2261
 
0.4%
5081
 
0.4%
429/4301
 
0.4%
4761
 
0.4%
5441
 
0.4%
Other values (225)225
95.7%

Most occurring characters

ValueCountFrequency (%)
2154
17.8%
4142
16.4%
5122
14.1%
393
10.7%
165
7.5%
954
 
6.2%
053
 
6.1%
652
 
6.0%
846
 
5.3%
741
 
4.7%
Other values (4)44
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number822
94.9%
Other Punctuation40
 
4.6%
Space Separator4
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
2154
18.7%
4142
17.3%
5122
14.8%
393
11.3%
165
7.9%
954
 
6.6%
053
 
6.4%
652
 
6.3%
846
 
5.6%
741
 
5.0%
ValueCountFrequency (%)
/34
85.0%
,4
 
10.0%
\2
 
5.0%
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common866
100.0%

Most frequent character per script

ValueCountFrequency (%)
2154
17.8%
4142
16.4%
5122
14.1%
393
10.7%
165
7.5%
954
 
6.2%
053
 
6.1%
652
 
6.0%
846
 
5.3%
741
 
4.7%
Other values (4)44
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII866
100.0%

Most frequent character per block

ValueCountFrequency (%)
2154
17.8%
4142
16.4%
5122
14.1%
393
10.7%
165
7.5%
954
 
6.2%
053
 
6.1%
652
 
6.0%
846
 
5.3%
741
 
4.7%
Other values (4)44
 
5.1%

Long
Real number (ℝ≥0)

Distinct35
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.077585331
Minimum2.061
Maximum2.101
Zeros0
Zeros (%)0.0%
Memory size5.7 KiB
2021-02-24T13:18:47.926004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.061
5-th percentile2.065
Q12.069
median2.073
Q32.085
95-th percentile2.099
Maximum2.101
Range0.04
Interquartile range (IQR)0.016

Descriptive statistics

Standard deviation0.01125287706
Coefficient of variation (CV)0.005416324851
Kurtosis-0.7580980756
Mean2.077585331
Median Absolute Deviation (MAD)0.007
Skewness0.7187212963
Sum1473.008
Variance0.0001266272422
MonotocityNot monotonic
2021-02-24T13:18:48.384456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2.07277
 
10.9%
2.06642
 
5.9%
2.09841
 
5.8%
2.09939
 
5.5%
2.07438
 
5.4%
2.06537
 
5.2%
2.06937
 
5.2%
2.08535
 
4.9%
2.06833
 
4.7%
2.06732
 
4.5%
Other values (25)298
42.0%
ValueCountFrequency (%)
2.0611
 
0.1%
2.0622
 
0.3%
2.0631
 
0.1%
2.06427
3.8%
2.06537
5.2%
ValueCountFrequency (%)
2.1016
 
0.8%
2.09939
5.5%
2.09841
5.8%
2.09720
2.8%
2.0967
 
1.0%

Lat
Real number (ℝ≥0)

Distinct14
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.89898449
Minimum48.888
Maximum48.905
Zeros0
Zeros (%)0.0%
Memory size5.7 KiB
2021-02-24T13:18:48.478607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum48.888
5-th percentile48.894
Q148.896
median48.9
Q348.901
95-th percentile48.904
Maximum48.905
Range0.017
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.003354908146
Coefficient of variation (CV)6.860895336 × 105
Kurtosis0.3426688387
Mean48.89898449
Median Absolute Deviation (MAD)0.002
Skewness-0.6027205388
Sum34669.38
Variance1.125540867 × 105
MonotocityNot monotonic
2021-02-24T13:18:48.552647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
48.9120
16.9%
48.90187
12.3%
48.89678
11.0%
48.90362
8.7%
48.90260
8.5%
48.89960
8.5%
48.89753
7.5%
48.89548
 
6.8%
48.89436
 
5.1%
48.90433
 
4.7%
Other values (4)72
10.2%
ValueCountFrequency (%)
48.88812
 
1.7%
48.89317
 
2.4%
48.89436
5.1%
48.89548
6.8%
48.89678
11.0%
ValueCountFrequency (%)
48.90513
 
1.8%
48.90433
 
4.7%
48.90362
8.7%
48.90260
8.5%
48.90187
12.3%

Interactions

2021-02-24T13:18:26.608662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:26.700864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:26.793458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:26.882710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:26.966200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.056815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.151964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.246055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.331786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.419363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.499179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.576832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.661049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.749727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.836929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:27.929386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.017097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.103019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.186337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.276667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.371944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.465547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.548841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.627398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.710358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.784634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.866474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:28.952179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:29.036618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:29.116405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:29.195196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:29.291903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:29.391936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:29.499631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:29.611983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:29.721199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:29.850097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:29.966797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.066672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.149969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.231225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.323756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.419405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.517559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.609715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.706544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.796322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.884215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:30.978836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:31.076624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:31.170456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:31.260239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:31.354771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:31.443759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:31.529209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-24T13:18:31.621178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-24T13:18:48.638437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-24T13:18:48.781994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-24T13:18:48.925142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-24T13:18:49.119679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-24T13:18:31.915624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-24T13:18:34.210844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-24T13:18:35.781616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-24T13:18:36.588479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ID_ARBREcommunequartiersitecote_voiriematricule_arbregenre_arbreespece_arbrecontrolesituationtype_solsurf_permeabledate_plantationclasse_agehauteurclasse_hauteurdiametrecirconference (en cm)classe_circonferenceport_arbrevigueur_poussechampignon_colletinsecte_colletplaie_colletobservation_colletchampignon_troncinsecte_troncfissure_troncrejet_tronctuteurage_arbrecanisse_arbreplaie_troncobservation_troncchampignon_houppierinsecte_houppierfissure_houppierecorce_incluse_houppierbois_mort_houppierplaie_houppierobservation_houppieresperance_maintiencontrainteclassification_diagnosticdate_diagnosticprescription_1prescription_2prescription_3observation_travauxtype_delai_1delai_annee_programmationtype_delai_2delai_preconisation_2delai_saison_programmation_2delai_annee_programmation_2reference_photoLongLat
078551-Arbres-001Saint Germain en LayeQuartier 2 - Alsace - PereireCarrefour RN13NaN7BetulaAlbaNaNGroupeP5.02000A1000H263.661977100C2SLPNonNonRCPLNSNaNNonNonTPFNonNonNonTPLNCNaNNonNonHPFNonHBMIHPLCNaN1.0NonC22020/10/22tdgNaNNaNNaNNaNNaNbNaNb42022.02592.07448.899
178551-Arbres-002Saint Germain en LayeQuartier 2 - Alsace - PereireCarrefour RN13NaN8BetulaAlbaNaNGroupeP5.02000A1000H260.47887895C2SLPNonNonRCPPLNaNNonNonTPFNonNonNonTPLCNaNNonNonHPFNonHBMIHPLCNaN1.0NonC22020/10/22tdgtsbmNaNNaNNaNNaNbNaNb42022.0NaN2.07448.899
278551-Arbres-003Saint Germain en LayeQuartier 2 - Alsace - PereireCarrefour RN13NaN9BetulaAlbaNaNGroupeP5.02000A1000H254.11268185C2SLPNonNonRCPPLNaNNonNonTPFNonNonNonTPLCFNaNNonNonHPFNonHBMIHPLCNaN1.0NonC22020/10/22tdgtdgNaNNaNNaNNaNbNaNb42022.0NaN2.07448.899
378551-Arbres-004Saint Germain en LayeQuartier 2 - Alsace - PereireCarrefour RN13NaN1CarpinusBetulus - L. - FastigiataNaNBosquetG100.01980A1600H463.661977100C2LPNonNonRCPPLCépéeNonNonTPFNonNonNonTPLSNaNNonNonHPFNonHBMIHPLSNaN1.0NonC22020/10/07tsbmNaNNaNNaNNaNNaNbNaNb42022.0NaN2.07348.900
478551-Arbres-005Saint Germain en LayeQuartier 2 - Alsace - PereireCarrefour RN13NaN2CarpinusBetulus - L. - FastigiataNaNBosquetG100.01980A1600H4190.985932300C6LPNonNonRCPPLCépéeNonNonTPFNonNonNonTPLSNaNNonNonHPFNonHBMIHPLSNaN1.0NonC22020/10/07tsbmNaNNaNNaNNaNNaNbNaNb42022.0NaN2.07448.899
578551-Arbres-006Saint Germain en LayeQuartier 2 - Alsace - PereireCarrefour RN13NaN3Betulasp. - -NaNGroupeG100.01990A1800H466.845076105C3LPNonNonRCPPLNaNNonNonTPFNonNonNonTPLSNaNNonNonHPFNonHPBMHPLSNaN1.0NonC22020/10/07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.07348.900
678551-Arbres-007Saint Germain en LayeQuartier 2 - Alsace - PereireCarrefour RN13NaN4Betulasp. - -NaNGroupeG100.01990A1800H457.29578090C2LPNonNonRCPPLNaNNonNonTPFNonNonNonTPLSNaNNonNonHPFNonHPBMHPLSNaN1.0NonC22020/10/07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.07348.900
778551-Arbres-008Saint Germain en LayeQuartier 2 - Alsace - PereireCarrefour RN13NaN5Betulasp. - -NaNGroupeG100.01990A1800H447.74648375C2LPNonNonRCPPLNaNNonNonTPFNonNonNonTPLSNaNNonNonHPFNonHPBMHPLSNaN1.0NonC22020/10/07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.07348.900
878551-Arbres-009Saint Germain en LayeQuartier 2 - Alsace - PereireCarrefour RN13NaN6PrunusCerasifera - Ehrh. - NigraNaNIsoléG100.01990A1000H247.74648375C2LPNonNonRCPPLNaNNonNonTPFNonNonNonTPLSNaNNonNonHPFNonHPBMHPLSAxe 3 Mort 2M Nord1.0NonC22020/10/07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.07348.900
978551-Arbres-010Saint Germain en LayeQuartier 2 - Alsace - PereireNouveau cimetièreNaN1FraxinusAngustifolia - L. - RaywoodNaNAlignementG100.02010JA1200H341.38028565C2LPNonNonRCPLSNaNNonNonTPFNonNonNonTPLSNaNNonNonHPFNonHPBMHPLSNaN1.0NonC12020/10/12tfoNaNNaNNaNNaNNaNbNaNb22022.0NaN2.06448.901

Last rows

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69978551-Arbres-700Saint Germain en LayeQuartier 7 - Debussy - SchnapperBois SchnapperNaN11AcerPseudoplatanus - L. -NaNBosquetCS100.01990A2200H570.028175110C3LPNonNonRCPLSNaNNonNonTFFNonNonNonTPLCFPlaie longitudinaleNonNonHFFNonHBMIHPLSNaN4.0OuiC42020/12/15abNaNNaNaccès difficileNaNNaNaa3NaNNaN515/5162.08848.888
70078551-Arbres-701Saint Germain en LayeQuartier 7 - Debussy - SchnapperBois SchnapperNaN12AcerPlatanoides - L. -NaNBosquetCS100.01990A2200H582.760570130C3LPNonNonRCPLSNaNNonNonTPFNonNonNonTPLSNaNNonNonHPFNonHBMIHPLNCCavité ouverte3.0NonC32020/12/15NaNNaNNaNaccès difficileNaNNaNNaNNaNNaNNaNNaN2.08848.888
70178551-Arbres-702Saint Germain en LayeQuartier 1 - Cœur de Ville et Quatier forestierPlace de la victoireNaN1PhotiniaxFraseri'RedRobin'NaNNaNAlignementGr1.02000A500H128.64789045C1SLPNonNonRCPPLRacine apparenteNonNonTPFNonNonNonTPPLTronc torsadéNonNonHPFNonHPBMHPPLBonne vigueur1.0NonC22020/12/18NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN555/5562.08948.899
70278551-Arbres-703Saint Germain en LayeQuartier 1 - Cœur de Ville et Quatier forestierPlace de la victoireNaN2PhotiniaxFraseri'RedRobin'NaNNaNAlignementGr1.02017J500H112.73239520C1SLPPNonNonRCPLNCChoc colletNonNonTPFNonT1:MonopodeNonTPLSÉchaudureNonNonHPFNonHPBMHPPLHouppier clairsemé1.0NonC22020/12/18RTNaNNaNNaNNaNNaNbNaNb22021.0557/5582.08948.899
70378551-Arbres-704Saint Germain en LayeQuartier 1 - Cœur de Ville et Quatier forestierPlace de la victoireNaN3PhotiniaxFraseri'RedRobin'NaNNaNAlignementGr1.02017J500H119.09859330C1SLMPNonNonRCPPLNaNNonNonTPFOuiNonOuiTPPLNaNNonNonHPFNonHPBMHPPLHouppier clairsemé1.0NonC22020/12/18RCNaNNaNNaNNaNNaNbNaNb22022.05592.08948.899
70478551-Arbres-705Saint Germain en LayeQuartier 1 - Cœur de Ville et Quatier forestierPlace de la victoireNaN4PhotiniaxFraseri'RedRobin'NaNNaNAlignementGr1.02000A500H122.28169235C1SLPNonNonRCPPLRacine apparenteNonNonTPFNonNonNonTPPLTronc torsadéNonNonHPFNonHPBMHPPLBonne vigueur1.0NonC22020/12/18NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN560/5612.08948.898
70578551-Arbres-706Saint Germain en LayeQuartier 1 - Cœur de Ville et Quatier forestierPlace de la victoireNaN5PhotiniaxFraseri'RedRobin'NaNNaNAlignementGr1.02017J500H112.73239520C1SLDNonNonRCPPLRacine apparenteNonNonTPFNonT1:MonopodeNonTPPLNaNNonNonHPFNonHPBMHPPLHouppier clairsemé3.0NonC22020/12/18RTNaNNaNNaNNaNNaNbNaNb22022.05622.08948.899
70678551-Arbres-707Saint Germain en LayeQuartier 1 - Cœur de Ville et Quatier forestierPlace de la victoireNaN6PhotiniaxFraseri'RedRobin'NaNNaNAlignementGr1.02000A500H125.46479140C1SLPNonNonRCPPLNaNNonNonTPFNonNonNonTPLNCPlaie longitudinaleNonNonHPFNonHPBMHPPLBonne vigueur1.0NonC22020/12/18NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN563/5642.08948.899
70778551-Arbres-708Saint Germain en LayeQuartier 1 - Cœur de Ville et Quatier forestierPlace de la victoireNaN7PhotiniaxFraseri'RedRobin'NaNNaNAlignementGr1.02019J500H115.91549425C1SLPPNonNonRCPPLRacine apparenteNonNonTPFNonT2:BipodeOuiTPPLNaNNonNonHPFNonHPBMHPPLHouppier clairsemé2.0NonC22020/12/18RTRCNaNNaNNaNNaNbNaNb22022.0566/5652.08948.899
70878551-Arbres-709Saint Germain en LayeQuartier 1 - Cœur de Ville et Quatier forestierPlace de la victoireNaN8PhotiniaxFraseri'RedRobin'NaNNaNAlignementGr1.02010JA500H122.28169235C1SLPNonNonRCPLNCRacine apparenteNonNonTPFNonT3TripodeNonTPLNCPlaie longitudinaleNonNonHPFNonHPBMHPPLBonne vigueur1.0NonC22020/12/18RTNaNNaNNaNNaNNaNbNaNb22022.0567/5682.09048.899